Threat Model: deepagents-code

Note: deepagents-code was forked from deepagents-cli at v0.1.0. References to "the CLI" throughout this document describe the deepagents-code runtime.

Generated: 2026-08-19 | Scope: libs/code only

Disclaimer: This threat model is automatically generated to help developers and security researchers understand where trust is placed in this system and where boundaries exist. It is experimental, subject to change, and not an authoritative security reference — findings should be validated before acting on them. The analysis may be incomplete or contain inaccuracies. We welcome suggestions and corrections to improve this document.

Scope

In Scope

deepagents_code/ — all Python source modules shipped as the deepagents-code package
CLI entry point (main.py, __init__.py)
Interactive TUI (app.py, tui/textual_adapter.py, tui/widgets/)
Non-interactive pipeline runner (client/non_interactive.py)
Agent creation (agent.py)
Built-in tools (tools.py: http_request, web_search, fetch_url)
MCP config loader and per-server allow/deny lists (mcp_tools.py, model_config.py)
Hook runtimes (hooks/)
Sandbox integration factory (integrations/sandbox_factory.py, integrations/sandbox_provider.py)
Session persistence (sessions.py)
Configuration system (config.py, model_config.py)
Unicode/URL safety helpers (unicode_security.py)
LangGraph dev server subprocess management (client/launch/server.py, client/launch/server_manager.py, server_graph.py)
Remote agent client (client/remote_client.py)
Local context middleware (local_context.py)
Custom subagent loader (subagents.py, agent.py:load_async_subagents)
Conversation offload (offload.py)
Skill management (skills/commands.py)
Python extension loading (extensions/)
Persisted goal/rubric state notices (goal_state_notice.py, goal_tools.py, goal_state_limits.py)

Out of Scope

libs/deepagents/ (SDK library) — separate package with its own threat model
libs/acp/, libs/evals/, libs/partners/ — separate packages
tests/ — not shipped code; used during analysis only
scripts/, examples/ — developer tooling, not shipped
Deployment infrastructure, CI/CD pipelines
LLM provider behavior (model outputs, jailbreaks) — user-controlled
Sandbox provider internals (Daytona, LangSmith, Modal, Runloop, AgentCore) — third-party
LangGraph server internals — consumed as a subprocess dependency

Assumptions

Throughout this document ~/.deepagents/ names the effective user profile directory. That is the default location; DEEPAGENTS_HOME selects a different one (TB14), and every claim about profile-owned files applies to whichever directory is selected at launch.

The CLI runs locally on the user's machine; the user is a developer who invoked deepagents themselves.
The project provides the HITL approval framework. Users control model selection, API keys, and whether to disable approval gates. Managed policy can narrow each of these; [models].allowed narrows model selection.
The user profile directory is only writable by the authenticated local user — no multi-user shared home directories. DEEPAGENTS_HOME (see TB14) can relocate that directory outside $HOME; keeping the selected location single-user is then the operator's responsibility.
Sandbox backends are trusted third-party services. CLI responsibility ends at correctly constructing and dispatching requests to them.
LangSmith tracing, if enabled, is user-opted-in via environment variables.
The LangGraph dev server subprocess binds to 127.0.0.1 by default (client/launch/server.py:_DEFAULT_HOST) and is ephemeral — started and stopped per CLI session.
DA_SERVER_* environment variables are readable only by the CLI process and its child server subprocess (OS process isolation assumption).
Users who set class_path in config.toml accept the same trust model as pyproject.toml build scripts — they control their own machine.
Administrators deploy and protect the fixed managed_config.toml path with operating-system controls; the CLI does not validate its owner or mode and never writes it.
User-directory, user-config, CLI, installed-plugin, and Python entry-point extensions are authorized by the user action that supplied them. Project extensions execute only after explicit, configured, or persisted trust.
Extension backend routes cannot overlap dcode's local artifact or conversation-history storage. A sandboxed agent rejects direct FilesystemBackend and LocalShellBackend mounts, including subclasses; wrapper implementations are responsible for their own isolation because dcode does not recursively inspect arbitrary backend object graphs.

System Overview

deepagents-code is a terminal-based AI coding assistant. It wraps the deepagents SDK in an interactive TUI (Textual) and a headless non-interactive mode. Both modes route agent execution through a local langgraph dev subprocess: the CLI spawns a server, passes configuration via DA_SERVER_* environment variables, and communicates via a RemoteAgent HTTP+SSE client. The agent receives user prompts, reasons with a configurable LLM, and executes side-effecting tools (file read/write, shell commands, web search, HTTP requests) subject to a human-in-the-loop (HITL) approval gate. Sessions are persisted in a local SQLite checkpoint database. Users can extend the agent with MCP servers (stdio processes or remote HTTP/SSE endpoints), hooks (event-driven subprocesses), custom subagents (AGENTS.md files in .deepagents/agents/), async remote subagents (LangGraph deployments configured in config.toml), and pluggable sandbox backends for remote code execution.

Architecture Diagram

┌──────────────────────────────────────────────────────────────────────┐
│                        User (local machine)                          │
│                                                                      │
│  CLI Args / Env Vars ──► C1: CLI Entry Point (main.py)               │
│                                  │                                   │
│                 ┌────────────────┼─────────────────────┐             │
│                 ▼                ▼                     ▼             │
│        C2: TUI (app.py)  C2b: Non-interactive   C13: Config Channel  │
│                 │      (client/non_interactive.py) (DA_SERVER_* vars) │
│ - - - - - - - - - - TB8: CLI / Server IPC - - - - - │ - - - - - - - │
│                 │                                     ▼              │
│                 │        C11: Server Manager (client/launch)          │
│                 │                      │ spawns                      │
│                 │                      ▼                             │
│                 │           C11b: LangGraph Dev Server               │
│                 │     (client/launch/server.py:ServerProcess)        │
│                 │           LANGGRAPH_AUTH_TYPE=noop                 │
│ - - - - - - - - │ - - - - - TB10: RemoteAgent / Dev Server - - - -  │
│                 │                      ▲                             │
│                 └─►C12: RemoteAgent────┘ (HTTP+SSE on 127.0.0.1)    │
│                     (client/remote_client.py)                        │
│                            │                                         │
│                 ┌──────────┴───────────┐                             │
│                 ▼                      ▼                             │
│       C18: Offload HTTP Boundary  C3: Agent Engine                   │
│       (custom route + operation)  (server_graph.py)                  │
│                 │                      │                             │
│                 └──────────┬───────────┘                             │
│                            │                                         │
│                     (create_cli_agent, deepagents SDK)               │
│                            │                                         │
│  User Prompt ──────────────┘                                         │
│                            │                                         │
│ - - - - - - - - - TB1: User→Agent Input - - - - - - - - - - - - -   │
│                            │                                         │
│                     LLM Decision                                     │
│ - - - - - - - - - TB2: LLM → Tool Execution (HITL) - - - - - - - -  │
│                            │                                         │
│   ┌──────────┬─────────┬───┴────────┬──────────┬──────────────┐     │
│   ▼          ▼         ▼            ▼          ▼              ▼     │
│  C4:Tools  C5:MCP   C6:Hooks     C8:Sessions C7:Sandbox  C14:Async  │
│  (file,    (procs/  (subprocs    (SQLite)    (Daytona/   Subagents   │
│  shell,    remote)  hooks.json)             Modal/etc.) (LangGraph   │
│  HTTP)                                                   remotes)   │
│   │          │                     │          │              │       │
│ - │ - - - - -│- - - - TB3 - - - - -│- - - - - │ - - - - - - -│ - -  │
│   │          ▼                     ▼          ▼              │       │
│   │      External               Local FS   Remote     External LG   │
│   ▼      MCP Server            (~/.deep   Sandbox    Deployment     │
│ External  (proc/net)           agents/)   API                       │
│ Web/APIs                                                             │
│ - - - TB4: Web content → Context - - - - - - - - - - - - - - - - -  │
│   Tool results re-enter agent context window                         │
│ - - - TB9: LocalContextMiddleware / Host environment - - - - - - -   │
│   C15: LocalContextMiddleware runs bash detect script                │
│                                                                      │
│  C9: Config System ──► C17: Model Config (class_path → importlib)   │
│  (config.toml, .env)                                                │
└──────────────────────────────────────────────────────────────────────┘

Components

ID	Component	Description	Trust Level	Default?	Entry Points
C1	CLI Entry Point	Parses argv, loads config/env, bootstraps session	framework-controlled	Yes	`main.cli_main`, `main.parse_args`
C2	TUI / Non-interactive	Textual UI for interactive chat; `client/non_interactive.py` for headless pipelines (both use `RemoteAgent`)	framework-controlled	Yes	`app.DeepAgentsApp.run`, `client.non_interactive.run_non_interactive`
C3	Agent Engine	LangGraph agent graph running inside `langgraph dev` server, assembled by `create_cli_agent`	framework-controlled	Yes	`agent.create_cli_agent`, `agent._add_interrupt_on`, `server_graph.make_graph`
C4	Built-in Tools	`http_request`, `web_search` (Tavily), `fetch_url` (HTML→markdown)	framework-controlled	Partial¹	`tools.http_request`, `tools.web_search`, `tools.fetch_url`
C5	MCP Loader & Trust	Discovers/loads `.mcp.json`, validates server configs, applies per-server allow/deny lists + interactive approval	framework-controlled	No²	`mcp_tools.resolve_and_load_mcp_tools`, `model_config.load_mcp_server_trust_lists`, `main._check_mcp_project_trust`
C6	Hook Runtime	Fires subprocess commands on agent lifecycle events	framework-controlled	No³	`hooks.runtime.HooksRuntime`, `hooks.runner.run_command_handler`
C7	Sandbox Integration	Creates/destroys remote sandboxes (Daytona, LangSmith, Modal, Runloop, AgentCore)	framework-controlled	No⁴	`integrations.sandbox_factory.create_sandbox`
C8	Session Persistence	SQLite checkpoint store for LangGraph thread state	framework-controlled	Yes	`sessions.get_db_path`, `sessions.generate_thread_id`
C9	Configuration System	Managed/user TOML, env vars, `AGENTS.md` system prompts, model config	administrator/user-controlled	N/A	`configuration`, `config.Credentials`, `config.RuntimeState`, `_paths.PATHS`, `model_config.ModelConfig`, fixed managed path, `~/.deepagents/config.toml`
C10	Unicode/URL Safety	Detects hidden Unicode, checks URL domain spoofing for approval UI warnings	framework-controlled	Yes	`unicode_security.detect_dangerous_unicode`, `unicode_security.check_url_safety`
C11	LangGraph Dev Server	Subprocess running `langgraph dev` with `LANGGRAPH_AUTH_TYPE=noop`; managed by `ServerProcess`	framework-controlled	Yes⁵	`client.launch.server.ServerProcess.start`, `client.launch.server.generate_langgraph_json`, `client.launch.server_manager.start_server_and_get_agent`
C12	Remote Agent Client	HTTP+SSE client wrapping `RemoteGraph`; connects to C11 on localhost	framework-controlled	Yes⁵	`client.remote_client.RemoteAgent.astream`, `client.remote_client.RemoteAgent.aget_state`
C13	Server Config Channel	Passes CLI config to server subprocess via `DA_SERVER_*` environment variables	framework-controlled	Yes	`_server_config.ServerConfig.to_env`, `_server_config.ServerConfig.from_env`
C14	Async Subagent Config	Loads remote LangGraph deployment specs from `[async_subagents]` in `config.toml`	user-controlled	No	`agent.load_async_subagents`
C15	LocalContext Middleware	Runs a bash detection script via backend; injects git/project/env context into system prompt each turn	framework-controlled	Yes⁶	`local_context.LocalContextMiddleware.before_agent`, `local_context.build_detect_script`
C16	Custom Subagent Loader	Reads `{dir}/{name}/AGENTS.md` YAML frontmatter from `.deepagents/agents/` and project `.agents/` directories	user-controlled	No	`subagents.list_subagents`, `subagents._parse_subagent_file`
C17	Model Config Loader	Resolves model providers, enforces the `models.allowed` policy (exact specs and `provider:*` wildcards), and supports `class_path` for arbitrary `BaseChatModel` instantiation via `importlib`	administrator/user-controlled	N/A	`config.create_model`, `config._create_model_from_class`, `model_config.ModelConfig.load`
C18	Server Offload Boundary	Custom HTTP route registered with LangGraph's route-auth layer (inert under the shipped `noop` auth, which relies on the loopback bind); reads thread state, runs the agent's shared compaction/hooks/backend, and commits a state-only result plus cost	framework-controlled	Yes for built-in graph⁷	`offload_api.offload`, `offload_api._execute_offload`, `offload_middleware.OffloadOperation.execute`
C19	Goal/Rubric State Notice	Projects persisted goal objectives, active criteria, and status notes into synthetic messages for the primary model	framework-controlled	Yes	`goal_state_notice.build_goal_state_notice`, `goal_tools.GoalToolsMiddleware`

Notes:

http_request and fetch_url enabled by default; web_search requires TAVILY_API_KEY.
MCP servers only load if .mcp.json config files are present.
User hooks load from ~/.deepagents/hooks.json. Project hooks load from .deepagents/hooks.json only after interactive workspace trust or the headless --trust-project-hooks opt-in.
Sandbox mode requires explicit --sandbox CLI flag.
Both TUI and non-interactive modes now always spawn a local LangGraph dev server and connect via RemoteAgent.
LocalContextMiddleware is added whenever LocalShellBackend or an _AsyncExecutableBackend is in use (agent.py:create_cli_agent).
Custom graph references do not receive dcode's HTTP app and do not support /offload.

Data Classification

ID	PII Category	Specific Fields	Sensitivity	Storage Location(s)	Encrypted at Rest	Retention	Regulatory
DC1	API Keys / Credentials	`ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, `TAVILY_API_KEY`, `LANGSMITH_API_KEY`, `LANGGRAPH_API_KEY`	Critical	Process environment only; never written to disk by CLI code	N/A (in-memory)	Process lifetime	All — breach trigger
DC2	Conversation Messages	User prompts, LLM responses, tool args/results, goal objectives, rubric criteria, and status notes	High	SQLite (`~/.deepagents/*.db`) via LangGraph checkpointer	No (local file, unencrypted)	Unbounded (session files persist)	GDPR if personal data is discussed
DC3	System Prompt Content	`DA_SERVER_SYSTEM_PROMPT` env var; custom AGENTS.md contents	Medium	Process environment (transient); `~/.deepagents/{agent}/AGENTS.md` on disk	No	Config lifetime	None direct
DC5	Offloaded Conversation History	Summarized + raw conversation messages written to sandbox backend	High	Sandbox filesystem at `/conversation_history/session_{uuid4hex}.md`	Depends on sandbox provider	Sandbox session lifetime	GDPR if personal data is discussed

Data Classification Details

DC1: API Keys / Credentials

Fields: ANTHROPIC_API_KEY, OPENAI_API_KEY, TAVILY_API_KEY, LANGSMITH_API_KEY, LANGGRAPH_API_KEY, and any provider-specific keys in the user's environment.
Storage: Loaded from environment at process start (config.py). The CLI explicitly strips LANGGRAPH_CLOUD_LICENSE_KEY and related auth vars from the server subprocess environment (client/launch/server.py:_build_server_env) but passes the remaining env vars (including provider API keys) to the server subprocess via os.environ.copy().
Access: Available to both the CLI process and the server subprocess (which inherits the full environment minus stripped vars).
Encryption: Not encrypted — in-memory process environment only.
Retention: Process lifetime; cleared on CLI exit.
Logging exposure: Provider SDK error messages may include partial key information. The CLI does not log keys directly.
Gaps: API keys are passed to the server subprocess via _build_server_env which does os.environ.copy() — all keys in the parent's environment become available to the child.

DC2: Conversation Messages

Fields: Full conversation history (HumanMessage, AIMessage, ToolMessage) stored as LangGraph checkpoint state, including synthetic notices that embed actionable goal objectives, active rubric criteria, and status notes.
Storage: SQLite at ~/.deepagents/{agent}/{thread_id}.db (via sessions.get_db_path).
Access: Local filesystem; readable by any process running as the same user.
Encryption: None — plaintext SQLite.
Retention: Unbounded — session files persist until manually deleted.
Logging exposure: Tool call arguments and results (including fetched web content) are in the checkpoint. File contents read by the agent are stored there too.
Gaps: Unencrypted on disk; no retention policy enforced by the CLI.

DC5: Offloaded Conversation History

Fields: Timestamped, formatted conversation messages written by the SDK's SummarizationMiddleware._aoffload_to_backend, reached through offload_middleware.CLICompactionMiddleware.
Producers: Three paths write this data.
- Automatic trigger-based compaction.
- The model-initiated compact_conversation tool (HITL-gated, see TB2).
- The explicit /offload command. This one is available only through C18 on a built-in server, which reads checkpoint state and writes the archive without entering the tool-approval path.
- Read guard: The server-owned /offload path wraps the backend in offload_middleware._ArchiveReadGuard, which fails closed rather than truncating existing history when its prerequisite read fails. The automatic and model-initiated paths write through the raw backend on the SDK's own code path. The guard is applied per write site rather than by the backend's type, so a new write site does not inherit it — see the _guarded_backend() call site.
Storage: Sandbox backend filesystem at path /conversation_history/session_{uuid4hex}.md. The leaf is the summarization session id (SummarizationMiddleware._get_history_path), not the thread id: it is minted per summarization session and persisted under _summarization_session_id so later compactions append to the same file. One thread can therefore own several archives.
Access: Accessible within the sandbox session; depends on provider access controls.
Encryption: Depends on sandbox provider storage backend.
Retention: Sandbox session lifetime (destroyed when sandbox is deleted).
Logging exposure: Contains full message history including tool results.
Gaps: The filename is a framework-minted session_<uuid4 hex> with no user-controlled component (no path injection risk), but offloaded content is unstructured markdown containing raw conversation data.

Trust Boundaries

ID	Boundary	Description	Controls (Inside)	Does NOT Control (Outside)
TB1	User Input → Agent Engine	Where user-typed prompts enter the agent graph	Prompt routing, session threading, UI rendering	Prompt content — any text accepted
TB2	LLM Decision → Tool Execution	HITL gate on all side-effecting tool calls	Interrupt map, allow-list check, auto-approve toggle	LLM reasoning; what user approves
TB3	Tool Result → LLM Context	Tool outputs re-enter the context window	Unicode warnings on URLs in args; markdownify HTML conversion	Content of fetched web pages, MCP responses, search results
TB4	MCP Config → Process / Network	`.mcp.json` triggers subprocess spawn or network connection	Schema validation; per-server allow/deny lists + interactive approval prompt	What the MCP process does once trusted and running
TB5	Hooks Config → Subprocess	`hooks.json` commands execute as local subprocesses	Workspace trust for project hooks, schema validation, bounded execution, sanitized environment	Command content (user-authored)
TB6	Setup Script → Sandbox Execution	User-supplied script runs inside sandbox at startup	`shlex.quote()` wrapping; `string.Template.safe_substitute`	Script content (user-authored); sandbox network access
TB7	External LLM API → Agent State	LLM API responses drive tool call decisions	LLM client configuration, model selection, request params	LLM output content; provider-side safety
TB8	CLI → Server Subprocess IPC	Config passed from CLI to `langgraph dev` subprocess via `DA_SERVER_*` env vars	`ServerConfig.to_env()` serialization; env var scoping to parent+child process	Subprocess environment post-fork; /proc visibility to same-uid processes
TB9	LocalContextMiddleware → Host Env	Bash detect script output (git info, project files, Makefile) injected into system prompt	Script is framework-generated static code; 30s timeout; exit-code check	Content of Makefile, pyproject.toml, git branch names, directory listing
TB10	RemoteAgent → LangGraph Dev Server	CLI communicates with agent via HTTP+SSE on localhost	Server bound to `127.0.0.1` (`client/launch/server.py:_DEFAULT_HOST`); ephemeral per session	No authentication (`LANGGRAPH_AUTH_TYPE=noop`); any localhost process can reach the API
TB11	Config File → Code Execution	`class_path` in `config.toml` triggers `importlib.import_module()`; project/global `.env` values are loaded into the process environment and can reach Bash startup hooks	Format validation (`module:ClassName`); `issubclass(BaseChatModel)` check; dotenv loading denies shell startup / environment-hijack keys (`BASH_ENV`, `ENV`)	Module-level side effects execute during import; user controls config file; project files in the working directory influence execution
TB12	Goal/Rubric State → Model Context	Persisted user- and agent-controlled goal state becomes a synthetic `HumanMessage` in a primary-model request	State projection, lifecycle filtering, notice fingerprinting, raw-character limits, HTML escaping of boundary tags	Natural-language instructions, sensitivity, post-escape size, and provider-specific byte/token budgets
TB13	Managed Config → Runtime	A fixed administrator-deployed TOML file overrides CLI, environment, and user preferences	Fixed non-redirectable path; the CLI never writes the file; fail-closed startup for every command except diagnostics; typed resolution; model-policy checks before credentials/imports/construction; diagnostics remain available	Filesystem ownership/mode and privileged deployment are outside the CLI; a host administrator can weaken or strengthen policy
TB14	Launch Env → User Trust Root	Inherited `DEEPAGENTS_HOME` selects the profile whose config, credentials, and user MCP file are trusted	Captured and normalized once before dotenv loading; absolute/`~/` validation; denied from every dotenv layer; propagated unchanged to the server	A user can deliberately select a profile inside or above a checkout; only the exact profile `.mcp.json` receives user provenance

Boundary Details

TB1: User Input → Agent Engine

Inside: app.DeepAgentsApp routes keystrokes to message queue; client.non_interactive.run_non_interactive reads from stdin/argv. Both paths now go through RemoteAgent.astream to the server. Session ID assigned per thread (sessions.generate_thread_id).
Outside: Prompt content — the agent accepts any text the user types. No content filtering at this layer (HITL handles downstream tool calls, not prompt intent).
Crossing mechanism: HTTP POST to 127.0.0.1:{port} via client.remote_client.RemoteAgent.astream.

TB2: LLM Decision → Tool Execution Gate (HITL)

Inside: agent._add_interrupt_on registers interrupt configs for execute, write_file, edit_file, web_search, fetch_url, task, compact_conversation, launch_async_subagent, update_async_subagent, cancel_async_subagent. In non-interactive mode, client.non_interactive._handle_action_request enforces the shell allow-list via config.is_shell_command_allowed.
Outside: Once the user clicks "approve" (interactive) or a command passes the allow-list check (non-interactive), the tool executes with no further framework-level gating.
Crossing mechanism: LangGraph HITL interrupt routed through RemoteAgent SSE stream.
Key note: auto_approve mode bypasses all HITL approval prompts while still displaying Unicode/URL warnings.
Key note: This boundary gates the model-initiated compact_conversation tool.
- Authorization: The explicit /offload command does not cross it. C18 invokes the agent's shared compaction service directly, with no tool node and no synthetic message. The slash command is the authorization.
- Hook events: The operation still dispatches PreCompact and PreToolUse against an in-memory forced call. Hooks may veto or interrupt. The TUI returns opaque hook replies over the operation protocol.
- ask is fail-closed: A PreToolUse ask decision cannot prompt on this path. The operation transport carries hook invocations, not HITL review requests, and interrupt() requires a Pregel task. _ask_permission_via_hitl therefore converts ask into a deny that carries the reason.
- Archive write: It reaches backend.awrite() without traversing tool approval. See DF25 and DF26.
- Unsupported deployments: Local in-process Pregel agents, including ACP mode, do not support /offload. Custom and older servers without C18 fail at the HTTP boundary rather than entering a client-driven tool path.
Key note: Only the pre hook events fire for /offload through C18. PostToolUse/PostToolUseFailure, which ServerHooksMiddleware records in awrap_tool_call and dispatches from _before_model via _maybe_post_tool_use on the next model turn, do not fire: there is no tool node to record the pending entry, and no following model turn to drain it. Likewise an allowing PreToolUse hook's additionalContext is discarded (logged, not injected): there is no tool result to carry it.

TB3: Tool Result → LLM Context

Inside: agent._format_execute_description / _format_fetch_url_description scan tool arguments (not results) for hidden Unicode and suspicious URLs. fetch_url converts HTML to markdown via markdownify before returning.
Outside: The content of tool results (fetched web pages, web search snippets, MCP tool responses, execute stdout) is passed verbatim into the LLM context window. No prompt-injection scanning of results.
Crossing mechanism: LangGraph ToolMessage returned to agent graph state; streamed to CLI via SSE.

TB4: MCP Config → Process / Network

Inside: mcp_tools._validate_server_config validates JSON structure and field types. Discovery emits typed (path, scope, project_root) records; runtime and login preserve that provenance instead of inferring trust from path containment. Only the exact configured profile .mcp.json is a user candidate. Standard project-root and .deepagents/.mcp.json candidates remain project-scoped when DEEPAGENTS_HOME is an ancestor of, inside, or equal to the checkout; a user/project path or symlink collision fails closed to project scope. Project-level servers (stdio and remote) require approval: an interactive prompt (main._check_mcp_project_trust) that offers allow-for-this-session ("y"), remember a chosen subset of servers ("r"), or deny ("N") — unless --trust-project-mcp is set, which trusts the whole config for the run. Whole-config trust is never persisted; it exists only as in-memory run state, set by --trust-project-mcp or by an interactive allow-once/remember decision. The remembered subset governs only what is persisted for future runs — both allow-once and remember load every prompted server for the current session. Persistent "remember" (a.k.a. "always allow") decisions are stored in [mcp].enabled_project_server_approvals with a local-project identity, server name, and server definition fingerprint. Remote server approvals with fixed URLs are shared by main checkouts and linked worktrees only through validated reciprocal Git common-directory metadata; independent clones remain separate, and non-Git or malformed/forged metadata falls back to the exact resolved root. Remote definitions with interpolated URLs remain exact-worktree scoped because project .env files can resolve the same template to different endpoints. Local stdio server approvals always use the exact resolved worktree root because the same command can execute different files in another checkout. A changed command, URL, or transport under the same name requires re-approval. Users can reject individual project server names via [mcp].disabled_project_servers (or DEEPAGENTS_CODE_DISABLED_PROJECT_MCP_SERVERS). There is also an explicit process-wide escape hatch, DEEPAGENTS_CODE_DANGEROUSLY_ENABLE_PROJECT_MCP_SERVERS, that approves matching names globally and intentionally bypasses the project/fingerprint binding. Reject wins over approval and over full trust, with one documented exception: if the user's config.toml is unreadable (see below), any deny defined only there is lost, so a name that is both TOML-disabled and exported in DEEPAGENTS_CODE_DANGEROUSLY_ENABLE_PROJECT_MCP_SERVERS survives — an accepted footgun (load_mcp_server_trust_lists documents it inline) that requires a self-contradicting config plus the explicit dangerous opt-in, and the read error is surfaced to the user. These policies are read by model_config.load_mcp_server_trust_lists only from user-controlled sources — the user-level profile config.toml, its global .env, and shell-exported env — never from .mcp.json or any repo-committed file, so a committed config cannot self-approve its own servers. In particular, the trust-list env forms are denied from project dotenv, while DEEPAGENTS_HOME is denied from every dotenv source under TB14. The dangerous enable env var and scoped TOML approvals are independent grants: setting the env var, including to an empty value, does not suppress remembered approvals. Disabled unions TOML and env so a deny can never be silently emptied by the other source. If the user's config.toml exists but cannot be read or parsed, the loader fails closed (project configs are treated as untrusted and the error is surfaced) rather than proceeding with an empty deny list. Only scoped-approved, dangerously env-enabled, or fully trusted names survive into the merged config, so a non-approved remote entry is never preflighted and its interpolated headers are never resolved.
Outside: Stdio server command, args, and env fields are user-controlled strings passed directly to StdioConnection. The env dict from MCP config is forwarded without filtering — users can set arbitrary environment variables (including PATH, LD_PRELOAD, PYTHONPATH) for the MCP subprocess.
Crossing mechanism: subprocess.Popen (via langchain_mcp_adapters) for stdio; HTTP/SSE for remote servers.

TB8: CLI → Server Subprocess IPC

Inside: ServerConfig.to_env() serializes all config (model, assistant_id, system_prompt, sandbox settings, cwd, MCP config path, shell-enable flags) to DA_SERVER_* env vars. client.launch.server_manager._apply_server_config writes these to os.environ before subprocess.Popen. client/launch/server.py:_build_server_env strips cloud auth vars (LANGGRAPH_CLOUD_LICENSE_KEY, LANGSMITH_CONTROL_PLANE_API_KEY, LANGSMITH_TENANT_ID, LANGGRAPH_AUTH).
Outside: The full parent environment including provider API keys flows to the child via os.environ.copy(). The system prompt string becomes DA_SERVER_SYSTEM_PROMPT. After process start, the env is fixed — no runtime mutation across processes.
Crossing mechanism: subprocess.Popen env kwarg; subsequent os.environ reads in _server_config.ServerConfig.from_env.

TB9: LocalContextMiddleware → Host Environment

Inside: local_context.build_detect_script generates a static bash heredoc that runs git commands, checks for lock files, reads Makefile headings, and produces a structured markdown summary. Script is static framework code — not constructed from user input. Exit code checked via _handle_detect_result. 30-second timeout.
Outside: The script reads and includes the first 20 lines of Makefile (_section_makefile) and a directory listing. These file contents are not sanitized before injection into the system prompt. An attacker who can write to the CWD's Makefile can influence the system prompt.
Crossing mechanism: backend.execute(DETECT_CONTEXT_SCRIPT) → result appended to system_prompt via LocalContextMiddleware._get_modified_request.

TB10: RemoteAgent → LangGraph Dev Server

Inside: Server bound to 127.0.0.1 by default; client/launch/server.py:_DEFAULT_HOST = "127.0.0.1". RemoteAgent only connects to the URL returned by ServerProcess.url. Server is ephemeral — started at session start, stopped at session end. Binds a free ephemeral port by default (client/launch/server.py:_EPHEMERAL_PORT); an explicit port is honored but still falls back to a free port if occupied.
Outside: LANGGRAPH_AUTH_TYPE=noop disables all LangGraph server authentication. Any process on localhost that discovers the port can submit requests, read thread state, or inject messages.
Crossing mechanism: HTTP POST/GET to http://127.0.0.1:{port} using langgraph.pregel.remote.RemoteGraph for graph operations and the same configured HTTP client for C18.
Key note: Graph registration and the accepted payload are both narrowed here.
- Registration: The default built-in graph_ref registers one agent graph plus the C18 custom HTTP app. A custom graph_ref registers only its graph and does not support /offload.
- Accepted: Operation identity, model/hook context, and opaque hook replies.
- Rejected: Messages, checkpoint identifiers, graph names, and state updates. The server refuses any operation update whose channels fall outside OffloadStateUpdate.
- Checkpoint invariant: The server reads and hydrates checkpoint messages itself and rejects active, pending, or changed threads. Its final thread-state update is state-only and targets the latest checkpoint, so it cannot branch from a stale checkpoint and hide concurrently appended messages.
- Enforced by: offload_api._execute_offload and client.remote_client.RemoteAgent.aoffload.

TB11: Config File → Code Execution

Inside: config._create_model_from_class validates class_path format (module:ClassName), imports the module via importlib.import_module(), and checks issubclass(cls, BaseChatModel) before instantiation.
Outside: Module-level code in the imported module executes unconditionally during import_module(). The issubclass check only runs after import. Any side effects (file I/O, network calls, subprocess spawning) in the module's top-level scope execute before the type check.
Crossing mechanism: importlib.import_module(module_path) in config._create_model_from_class.
Inside (dotenv): config._load_dotenv loads project and global .env values with override=False and drops shell startup / environment-hijack keys (BASH_ENV, ENV, and related) so a project .env cannot register a script that Bash would source at startup. DEEPAGENTS_HOME is also denied from both layers because profile selection has already crossed TB14. The denylist is best-effort for execution-hook variables: it enumerates variables known to reach a code-execution consumer (a shell's startup hook, the dynamic linker, an interpreter's startup path) and cannot claim completeness — any tool dcode spawns that honors an environment-driven execution hook not yet in the list remains a live vector until the key is added. The preview path (_preview_dotenv_environ) applies the same denylist so a dry-run config change cannot report a value a real reload would reject.
Outside (dotenv): All other .env keys are applied to the process environment, and any project file in the working directory (.env, Makefile, build scripts) can still influence execution. See T12.

TB12: Goal/Rubric State → Model Context

Inside: goal_state_notice.project_goal_state only exposes an objective and status note while the goal is actionable, and build_goal_state_notice HTML-escapes embedded text before wrapping it in boundary tags. GoalToolsMiddleware fingerprints notices, appends a fresh notice when state is stale or compacted, and re-pins the notice into model requests when necessary. Earlier bounded notices remain unchanged for prompt-cache stability; oversized legacy notices are replaced transiently with bounded same-index stand-ins, and each fresh notice explicitly supersedes them.

Outside: Goal objectives and criteria originate from user input. /rubric file reads the selected local text file. Status notes can be model-authored through update_goal. goal_state_limits rejects raw text above these limits:

Value	Limit (characters)
Goal objective	8,000
Rubric or acceptance criteria	12,000
Accepted objective and criteria combined	12,000
Status note or prior blocker	4,000
Total text across one notice	16,000

These counts do not account for expansion during HTML escaping, provider tokenization, or a provider-specific request budget. Labels such as "context data, not instructions" and escaping preserve message structure. They do not prevent the model from interpreting natural-language content as instructions. This flow has no provider-transmission confirmation.

Crossing mechanism: DeepAgentsApp._persist_goal_rubric_state writes state and notices to the checkpoint; GoalToolsMiddleware._notice_update and _request_with_goal_notice append them to the persisted or transient model-message list.

TB13: Managed Config → Runtime

Inside: The resolver reads one fixed OS path. The writer rejects that path, so the managed source is read-only by guard and not only by convention. Valid managed values take the highest precedence. The rules are:
- Tables deep-merge. Deny lists union. An explicit managed allow or trust list replaces lower-precedence grants.
- [models].allowed is a local-model ceiling of exact provider:model specs and provider:* wildcards. Discovery and selector filtering improve usability only.
- create_model is authoritative. It checks the canonical provider:model before credential bridging, provider hooks, imports, or constructors, so a blocked specification touches no stored key and runs no provider hook.
- Preflight checks on text a user typed resolve a bare name to the same canonical form first, so they neither reject a model that construction would allow nor accept one it would block. A name whose provider cannot be established stays unmatchable.
- create_cli_agent checks every model string it forwards: the primary model, the Auto classifier, the rubric grader, and an explicit local-subagent model. The SDK resolves a string through init_chat_model, which does not pass through create_model. A prebuilt model object came from a path that already checked.
- Runtime-context switches are rechecked server-side. A policy denial propagates instead of falling back to the previous model.
- A malformed [models].allowed blocks all model use at either layer. The managed layer also refuses to start.
- Remote async-subagent deployments select their models outside this local boundary. Tool enumeration also skips the check, because it compiles a graph it never invokes.
- A managed scalar replaces a colliding user table at any depth, so a user cannot defeat policy by changing the shape of a key. This holds on the top-level merge and inside a structured table: both apply the manifest validator, so the effective value and the audited provenance agree.
- A wrong-typed managed scalar is skipped, and the lower-precedence value stays in effect.
- Inside a structured table ([models.providers], [themes], [async_subagents], [sandboxes.providers], [ui.terminal_themes], [threads.columns]) the dedicated typed reader validates instead. A wrong-typed managed leaf there can displace a valid user leaf, after which the reader falls back to the built-in default.
- An enforced key whose managed value cannot be applied stops every command except the diagnostics listed below. It also blocks /reload. Skipping it would leave the user's CLI flag or environment variable in force. The enforced keys are startup.mode, startup.yolo_switcher, shell.allow_list, skills.extra_allowed_dirs, interpreter.enable_interpreter, interpreter.ptc, interpreter.ptc_acknowledge_unsafe, models.allowed, models.auto_classifier, runtime.recursion_limit, sandboxes.default, and tracing.langsmith_redact.
- Three conditions make an enforced key unapplicable: a wrong-typed value, a runtime.recursion_limit outside its bounds, and a key made unreachable by a scalar ancestor (startup = "manual" in place of [startup] and mode). A managed [sandboxes].default that names an unavailable backend stops a sandboxed launch.
- A scalar at a known section is rejected for the same reason, so it cannot replace the user's whole section. [effort] is included, although it has no manifest option.
- Any other rejected managed value is ignored, and dcode doctor and dcode config name it. An ignored key is never silent.
- A missing file is accepted and applies no policy.
- A present unreadable, undecodable, or syntactically corrupt file blocks every command except --help, --version, help, config, doctor, and auth path. It also blocks /reload.
- On Windows, a ProgramData directory that cannot be read from the registry blocks the same commands. The path would be a guess, and an empty read at a guessed path does not prove that no policy is deployed. Reporting it as a missing file made every managed setting silently inert on a host whose ProgramData is relocated.
- A failed /reload keeps the last enforceable snapshot, so policy is never dropped mid-session. A snapshot that parses but cannot be enforced is not cached either, so a rejected edit cannot become the generation later readers observe.
- An unusable user config.toml drops only the user layer, so managed policy still applies.
- A deny list that cannot be read is treated as denying everything, never as empty. This covers a managed [mcp].disabled_servers that is neither an array of names nor a comma-separated string, and a [mcp] section that is not a table. A managed [mcp].enabled_project_server_approvals that is not an array is treated the same way: the key is present, so policy means to narrow access, and reading its presence as absence would keep both the user's approvals and the DEEPAGENTS_CODE_DANGEROUSLY_ENABLE_PROJECT_MCP_SERVERS bypass in force.
Outside: Ownership, permission-mode validation, privileged installation, and sudo policy are deployment responsibilities. Anyone who can replace the administrator-managed file can control model, sandbox, interpreter, MCP trust, and other supported runtime settings. Model policy constrains official dcode selection and construction paths; it does not stop a hostile local user from replacing the package, modifying the process, or calling a provider SDK directly. On macOS, note that stock /Library/Application Support is group-writable by admin, so any admin-group member can create the file and grant themselves policy. Deployments that rely on this boundary must tighten ownership and mode on the dcode directory.
Remote source: The fixed local file may contain only [managed_config].source. Its administrator-owned HTTPS URL is then the explicit destination allowlist. The remote document becomes the whole managed policy. It cannot chain to another source. URLs with userinfo, query strings, or fragments are rejected. So are URLs carrying whitespace, control characters, or any non-ASCII character: confusable host text must not reach operator output, and urllib does not encode a non-ASCII request target consistently.
- Requests validate the certificate against Python's default TLS trust store, bypass environment proxies, and refuse redirects. One five-second budget covers the whole fetch: the connection, the TLS handshake, the headers, and the body. The fetch is abandoned once that budget is spent. At most 1 MiB is accepted.
- Only a complete document is accepted. The status must be 200. The media type must be application/toml, text/plain, or application/octet-stream, and the body must not be compressed. A captive portal or gateway error page that answers 200 with text/html is rejected before it is parsed. This is not a complete guard, and it is not relied on as one: a response with no Content-Type, or one claiming application/octet-stream, is read, because a bare object store answers that way. Such a body still has to parse as TOML and satisfy every rule below. Framing must show the body arrived whole: one matching Content-Length, or chunked encoding. A response that sends both, or that repeats Content-Length with different values, is rejected — the first value wins in http.client, so a duplicate header would otherwise let a prefix pass as a whole document. A keyless document is rejected as a failed publish. Truncated TOML often still parses, so a partial document would otherwise enforce a policy with entries silently missing, and would replace the last enforceable generation.
- The proxy bypass stops a local user redirecting the fetch through HTTPS_PROXY. The trust store itself is not pinned. SSL_CERT_FILE and SSL_CERT_DIR still select the certificate authorities, so the same actor can substitute the CA set for this request. This is the same deployment responsibility as the local file ownership in the Outside bullet above. This boundary does not apply on a host where an unprivileged user controls the dcode process environment.
- No disk cache or authentication material is stored. Private enterprise hosts remain reachable because the local trust anchor, not user input, selects the host. A fetch failure stops startup. A failed reload keeps the process's last enforceable snapshot, and dcode doctor reports that the refresh failed so a host that stopped answering is not silent.
Crossing mechanism: Synchronous local file read through configuration.TomlFileProvider, optionally followed by one bounded HTTPS fetch through configuration.RemoteTomlProvider, then typed resolution and CLI/server startup gates.

TB14: Launch Environment → User Trust Root

Inside: _paths.PATHS captures DEEPAGENTS_HOME before either dotenv layer is inspected. Empty/unset selects the launch user's ~/.deepagents; absolute paths and a leading ~/ are normalized without requiring the target to exist. Other relative paths and ~user forms stop startup, as do a dangling symlink and a root that exists but denies traversal. The unreadable case is rejected explicitly: Path.is_symlink reports False under EACCES, so it would otherwise pass every guard and fail one file at a time later. The normalized absolute value is frozen for the client and explicitly copied into every server subprocess environment, so reloads, cwd changes, and server cwd differences cannot move the profile.
Trust invariant: Project-controlled input cannot relocate the user trust root. DEEPAGENTS_HOME is in the deny set shared by project and global dotenv loading. MCP discovery separately records user/project provenance and gives project scope precedence when standard discovery locations collide. $HOME itself is rejected at startup, in any spelling: the comparison uses device and inode, so a symlink or a case variant on a case-insensitive filesystem is caught too. The home comparison fails closed: when identity cannot be determined at all, startup stops rather than accepting the path. For the roots that are accepted — an ancestor of a checkout, the checkout root, or a directory inside it — collision handling keeps the checkout's standard MCP files project-scoped. PATH is treated the same way: the installation-scoped managed bin may be prepended directly, but a verified rg in the profile fallback is exposed through a process-private shim containing only that entrypoint. A repository-controlled <profile>/bin and every sibling executable in it stay off PATH for agent subprocesses.
Outside: A shell or process supervisor can deliberately select any accepted absolute profile path at launch. Protecting ownership and permissions on that selected directory is the operator's responsibility.
Crossing mechanism: Inherited process environment → immutable standard-library-only path snapshot → normalized child environment.

Data Flows

ID	Source	Destination	Data Type	Classification	Crosses Boundary	Protocol
DF1	User	C2 TUI	User prompt text	—	TB1	Keystrokes / stdin
DF2	C2 TUI	C12 RemoteAgent	Human message + thread config	—	TB1	Function call
DF3	C12 RemoteAgent	C11 LangGraph Dev Server	Input messages, thread ID	DC2	TB10	HTTP POST (localhost)
DF4	C11 LangGraph Dev Server	C12 RemoteAgent	SSE stream (AI responses, tool calls, interrupts)	DC2	TB10	HTTP+SSE (localhost)
DF5	C3 Agent	External LLM	System prompt + message history	DC1, DC2	TB7	HTTPS / LangChain
DF6	External LLM	C3 Agent	AI response + tool call decisions	—	TB7	HTTPS / LangChain
DF7	C3 Agent	C4 Tools	Tool call arguments (file/URL/command)	—	TB2	Function call + HITL
DF8	External	C4 Tools	HTTP response bodies (web/API content)	—	TB3	HTTPS
DF9	C4 Tools	C3 Agent	Tool results (file content, web pages)	—	TB3	ToolMessage
DF10	C9 Config	C1 Entry	TOML config, env vars, API keys	DC1	None	File + environ
DF11	C5 MCP	C3 Agent	MCP tool call results	—	TB3, TB4	MCP protocol
DF12	C9 Config	C5 MCP	`.mcp.json` server definitions (command, args, env)	—	TB4	File read
DF13	C3 Agent	C8 Sessions	Agent state snapshots	DC2	None	SQLite async write
DF14	C8 Sessions	C3 Agent	Restored thread state on resume	DC2	None	SQLite async read
DF15	C9 Config	C6 Hooks	`hooks.json` command definitions	—	TB5	File read
DF16	C3 Agent	C6 Hooks	Event payload (JSON)	—	TB5	subprocess stdin
DF17	User	C7 Sandbox	Setup script path + content	—	TB6	File read + execute
DF18	C1 Entry	C11 Server	`DA_SERVER_*` env vars (config + credentials)	DC1, DC3	TB8	Process environment
DF19	Host FS	C15 LocalContext	Makefile, project file contents (first 20 lines), directory listing	DC3	TB9	bash subprocess stdout
DF20	C15 LocalContext	C3 Agent	Project context markdown appended to system prompt	DC3	TB9	string append to prompt
DF21	C16 Subagent Loader	C3 Agent	AGENTS.md body (raw text) used as subagent system_prompt	DC3	None	YAML parse + dict
DF22	C14 Async Config	C3 Agent	AsyncSubAgent specs (URL, graph_id, headers) from config.toml	—	None	TOML parse + dict
DF23	C9 Config	C17 Model Config	`class_path` string from `config.toml`	—	TB11	TOML parse → importlib
DF24	C5 MCP Config	MCP Subprocess	`env` dict from `.mcp.json` forwarded to stdio subprocess	DC1	TB4	subprocess environment
DF25	C3 Agent, C18 Server Offload Boundary	C7 Sandbox	Conversation messages for offload	DC5	TB6	`backend.awrite()`
DF26	C12 RemoteAgent	C18 Server Offload Boundary	Thread ID, operation identity, model/hook context, opaque hook replies; typed result or hook request	—	TB10	HTTP+JSON (localhost)
DF27	C18 Server Offload Boundary	C8 Sessions	Checkpoint message read; summarization event and additive cost update (never a messages write)	DC2	None	In-process LangGraph SDK
DF28	User / Host FS	C19 Goal/Rubric State Notice	Goal objective, criteria, and status notes; `/rubric file` content	DC2	TB1, TB12	TUI command + local file read + checkpoint update
DF29	C19 Goal/Rubric State Notice	External LLM	Synthetic user-role message containing actionable objective, active criteria, and status note	DC2	TB12, TB7	LangChain model request over configured provider transport
DF30	Administrator	C9 Config	Managed TOML policy	DC1	TB13	Fixed local file read

Flow Details

DF8/DF9: External Web Content → Agent Context

Data: Arbitrary HTML/JSON from the internet, converted to markdown by markdownify. Can be megabytes.
Validation: URL domain checked for Unicode spoofing / script mixing (unicode_security.check_url_safety); displayed as warning in approval dialog. Content not scanned for prompt-injection patterns.
Trust assumption: User approved the fetch. Content is data — but the LLM may interpret adversarial content as instructions.

DF11: MCP Tool Results → Agent Context

Data: Arbitrary strings/objects from MCP tool calls.
Validation: MCP config approved at load time (per-server allow-list or interactive prompt). Tool result content not validated after server is trusted.
Trust assumption: User trusted the MCP server; its outputs are as reliable as the server.

DF18: DA_SERVER_* Env Vars

Data: Model name, system prompt text, sandbox settings, CWD, MCP config path, shell-enable flags — plus inherited provider API keys.
Validation: No validation on server side beyond TOML/JSON decoding for structured fields (_read_env_json). The system prompt and model name are passed through verbatim.
Trust assumption: The server subprocess is trusted with the same access as the CLI process.

DF19/DF20: LocalContextMiddleware Bash Script

Data: Git branch/status, project language/structure, Makefile first 20 lines, directory listing (up to 20 files), runtime versions.
Validation: Script exit code checked (_handle_detect_result). 30-second timeout. Script itself is static framework code — not interpolated from user input.
Trust assumption: Files in the working directory are trustworthy. A malicious Makefile could inject content into the system prompt (requires write access to CWD).

DF23: class_path Config → importlib Code Execution

Data: Fully-qualified Python class path string (e.g., my_package.models:MyChatModel) from [models.providers.<name>] section of config.toml.
Validation: Format check (module:ClassName with : separator). After import, issubclass(cls, BaseChatModel) check. No validation of module contents before import.
Trust assumption: User controls ~/.deepagents/config.toml (same trust model as pyproject.toml build scripts).

DF24: MCP Stdio Env Dict → Subprocess

Data: Arbitrary key-value pairs from the "env" field of stdio server definitions in .mcp.json.
Validation: Type check only — env must be a dict (mcp_tools._validate_server_config). No filtering of key names or values. Forwarded directly to StdioConnection which passes to subprocess.Popen.
Trust assumption: User authored or approved the MCP config. Project-level configs go through the approval gate (allow-list or interactive prompt) before loading.

DF28/DF29: Goal/Rubric State → Primary-Model Context

Data: User-entered goal objectives and rubric criteria, full text loaded through /rubric file, and agent-written completion or blocker notes. These are persisted in checkpoint state and embedded in a synthetic HumanMessage whenever the current notice must be restored or re-pinned.
Validation: Direct, file-loaded, generated, and tool-authored goal-state paths enforce raw-character limits: 8,000 for an objective, 12,000 for a rubric, 12,000 for an accepted objective and criteria combined, 4,000 for a status note or prior blocker, and 16,000 across a notice. goal_state_notice._embedded_text then escapes <, >, and & to prevent boundary-tag forgery; lifecycle projection suppresses a paused or complete goal's objective. The scoped code has no content-safety, secret-detection, byte, token, or post-escape rendered-size limit.
Trust assumption: The user intentionally designates this content for model processing and accepts the configured provider's handling of the resulting request. Provider retention, location, and request authentication are configured outside this scoped flow.

Threats

ID	Data Flow	Classification	Threat	Boundary	Severity	Validation	Code Reference
T1	DF8, DF9	—	Prompt injection via fetched web content causes LLM to request harmful actions	TB3	Medium	Likely	`tools.fetch_url`, `agent._add_interrupt_on`
T2	DF7	—	`--shell-allow-list all` removes pattern checks; LLM-injected shell commands execute without approval in non-interactive mode	TB2	Medium	Verified	`config.is_shell_command_allowed`, `client.non_interactive._handle_action_request`
T3	DF7	—	Unicode-homoglyph URL in LLM-generated tool args deceives user during approval	TB2	Low	Disproven	`unicode_security.check_url_safety`, `agent._format_fetch_url_description`
T4	DF5, DF9	—	Auto-approve mode bypasses all HITL gates; any LLM-initiated tool call executes	TB2	Low	Verified	`agent.create_cli_agent` (`auto_approve` param), `agent._add_interrupt_on`
T5	DF13, DF14	DC2	Local SQLite checkpoint file tampered with to inject adversarial content into future LLM context	None	Low	Unverified	`sessions.get_db_path`
T6	DF3, DF4, DF26	DC2	Unauthenticated LangGraph dev server on localhost can be accessed by any local process	TB10	Medium	Verified	`server._build_server_env`, `server._DEFAULT_HOST`, `offload_api.app`
T7	DF19, DF20	DC3	Makefile or project file content injected into system prompt via LocalContextMiddleware	TB9	Low	Verified	`local_context._section_makefile`, `local_context.LocalContextMiddleware._get_modified_request`
T8	DF21	DC3	Custom subagent AGENTS.md body used verbatim as system_prompt without content validation	None	Low	Verified	`subagents._parse_subagent_file`, `agent.create_cli_agent`
T9	DF23	—	`class_path` in config.toml triggers arbitrary Python code execution via `importlib.import_module()`	TB11	Low	Verified	`config._create_model_from_class`, `model_config.ProviderConfig`
T10	DF24	DC1	MCP stdio subprocess env dict accepts arbitrary keys including `PATH`, `LD_PRELOAD`, `PYTHONPATH` without filtering	TB4	Low	Verified	`mcp_tools._validate_server_config`, `mcp_tools._load_tools_from_config`
T12	DF10	—	Project `.env` sets shell startup-hook variables (`BASH_ENV`, `ENV`) that run attacker-controlled scripts when `dcode` spawns Bash, before any HITL approval	TB11	High	Verified	`config._load_dotenv`, `local_context.build_detect_script`
T13	DF7	—	Configured shell allow-list checks only the first token, so an allow-listed interpreter/wrapper (`python3`, `bash`, `env`, `xargs`, …) runs arbitrary code via its arguments without approval in non-interactive mode	TB2	Medium	Verified	`config.is_shell_command_allowed`, `config.contains_dangerous_patterns`
T14	DF7, DF9	—	A weaker model configured for the Auto approval classifier reviews gated actions less reliably, including untrusted text carried in tool arguments and file content	TB2	Low	Verified	`auto_mode.AutoModeHITLMiddleware._classifier_model`, `config.resolve_auto_classifier_model`, `config_manifest.resolve_auto_classifier_timeout`
T15	DF28, DF29	DC2	Stored prompt injection through a goal, rubric, or status note influences later primary-model tool requests	TB12	Medium	Likely	`goal_state_notice.build_goal_state_notice`, `goal_tools.GoalToolsMiddleware._request_with_goal_notice`
T16	DF28, DF29	DC2	Sensitive local-file content, up to the 12,000-character rubric limit, is automatically persisted and transmitted to the configured model provider as rubric criteria	TB12	Medium	Verified	`app.DeepAgentsApp._set_rubric_from_file`, `goal_state_notice.build_goal_state_notice`
T17	DF28, DF29	DC2	Character-bounded goal/rubric/status-note text can still exceed provider context budgets after escaping or tokenization	TB12	Medium	Verified	`goal_state_limits`, `goal_state_notice.build_goal_state_notice`, `goal_tools.GoalToolsMiddleware._request_with_goal_notice`

Threat Details

T1: Prompt Injection via Fetched Web Content

Flow: DF8 (external web) → DF9 (tool result) → C3 Agent context
Description: When the agent calls fetch_url or web_search, the response body enters the LLM's context window as a ToolMessage. A maliciously crafted web page or search snippet can embed natural-language instructions that the LLM may interpret as authoritative commands, leading to unexpected tool call requests in the next turn.
Preconditions: (1) User or LLM-initiated call to fetch_url/web_search reaches a malicious page; (2) LLM interprets injected instructions as directives; (3) In interactive mode, user must still approve the resulting tool call.

T15: Stored Prompt Injection Through Goal/Rubric State

Flow: DF28/DF29 (user or local-file content → checkpointed notice → primary-model context)
Description: The goal-state notice embeds the full actionable objective, active criteria, and status note in a synthetic HumanMessage. A rubric loaded from an untrusted repository file, or a crafted status note, can therefore persist instructions that influence later model behavior. Bounded superseded notices remain in the append-only request history until compaction; the latest notice identifies itself as authoritative, but a model can still attend to older text. Oversized legacy notices are replaced only in the transient model request. HTML escaping and boundary labels prevent literal tag forgery. They do not stop natural-language prompt injection. Interactive HITL still gates side-effecting tool calls. Auto and non-interactive configurations can reduce that protection.
Preconditions: (1) The user accepts a goal/rubric or loads a file containing attacker-controlled instructions; (2) the state is actionable or the rubric remains active; (3) the primary model follows the injected content; (4) for side effects, the resulting tool call is approved or an approval-bypassing mode is active.

T16: Automatic Disclosure of File-Loaded Rubrics

Flow: DF28/DF29 (/rubric file → checkpoint → primary-model request)
Description: /rubric file reads the entire selected UTF-8 text file and persists its nonempty contents, up to the 12,000-character rubric limit (see TB12); a larger file is rejected outright rather than truncated. The notice then embeds the criteria into primary-model context. There is no warning or confirmation specific to provider transmission, so a user can inadvertently select a secret-bearing or proprietary file. This flow handles user content, not provider credentials. Credential storage and provider retention are outside the scoped implementation.
Preconditions: (1) A user selects a file with sensitive content; (2) it becomes an active rubric; (3) a model request is made while the rubric is active.

T17: Provider Context Pressure Despite Character Limits

Flow: DF28/DF29 (character-bounded text → escaped notice → model request)
Description: Direct, file-loaded, generated, and tool-authored goal-state paths enforce raw-character limits before persistence or notice construction. HTML escaping happens afterward and can expand the rendered notice (for example, & becomes &), while provider tokenization and available context budgets vary. The middleware restores or re-pins the current notice after compaction. A valid near-limit notice therefore remains recurring model-request overhead. This increases spend. It can also contribute to a provider context-limit failure.
Preconditions: (1) A user, file, or model-supplied status note produces a valid near-limit notice; (2) its escaped or tokenized representation is large relative to the configured provider's available context; (3) the corresponding goal or rubric remains model-visible.

T2: Shell Allow-List Bypass via `SHELL_ALLOW_ALL`

Flow: DF7 (LLM tool call) → C4 Tools (execute)
Description: When --shell-allow-list all (or DEEPAGENTS_SHELL_ALLOW_LIST=all) is set, is_shell_command_allowed returns True for any non-empty command without invoking contains_dangerous_patterns. In non-interactive mode, any shell command the LLM requests executes unconditionally. Combined with T1, an attacker-controlled page could cause arbitrary command execution. A configured (non-all) allow-list narrows this but is not a robust boundary either — see T13.
Preconditions: (1) User has configured --shell-allow-list all; (2) Non-interactive mode; (3) Successful prompt injection via DF8/DF9.

T3: Unicode Homoglyph URL in Approval Dialog

Flow: DF7 (LLM-generated fetch_url args) → C2 TUI approval dialog
Description: An LLM influenced by adversarial input could generate a fetch_url call with a URL containing mixed-script or confusable characters visually identical to ASCII.
Preconditions: LLM generates a confusable URL (requires adversarial steering). check_url_safety detects mixed-script domain labels and strip_dangerous_unicode removes invisible BiDi/zero-width characters; warnings displayed in approval dialog. Classified Disproven as a project vulnerability — the UI warnings are the intended control.

T4: Auto-Approve Removes All Execution Safeguards

Flow: DF7 (all tool calls) when auto_approve=True
Description: When auto-approve is enabled (via --auto-approve flag or Shift+Tab in TUI), all tool calls including execute, write_file, edit_file, fetch_url, launch_async_subagent execute without user confirmation.
Preconditions: User explicitly enables auto-approve. Default is approval-required.

T5: Local SQLite Checkpoint Tampering

Flow: DF13/DF14 (session persist/restore)
Description: LangGraph checkpoints stored in ~/.deepagents/*.db contain the full conversation history and agent state. An attacker with local filesystem write access could inject adversarial messages that re-enter the LLM context on session resume.
Preconditions: Attacker has write access to the user's home directory — equivalent to a fully compromised user account.

T6: Unauthenticated LangGraph Dev Server on Localhost

Flow: DF3/DF4/DF26 (CLI ↔ LangGraph dev server)
Description: The CLI spawns a langgraph dev server subprocess with LANGGRAPH_AUTH_TYPE=noop (client/launch/server.py:_build_server_env). This disables all server-side authentication. The server binds to 127.0.0.1:{port} (a free ephemeral port by default, so it no longer squats the well-known langgraph dev port 2024). Any local process that discovers the port can send inputs, read conversation state (including tool results that may contain file contents or secrets), inject messages, trigger state updates, or request server-owned offload for a known thread. The offload route does not accept conversation state and cannot write messages, so its direct impact is additional model/archive work plus a state-only summarization update. The server is ephemeral — it lives only for the duration of the CLI session — but this is the entire attack window. Port discovery is feasible via localhost port scanning or by reading /proc/{pid}/cmdline which contains the --port argument.
Preconditions: (1) Attacker has a local process running as the same user (or as root); (2) Attacker discovers the server port (port scan on localhost, or reads process arguments).

T7: LocalContextMiddleware Injects Host File Contents into System Prompt

Flow: DF19 → DF20
Description: LocalContextMiddleware runs a bash script (build_detect_script) that reads the first 20 lines of Makefile (_section_makefile) and a filtered directory listing, then injects this output verbatim into the system prompt on every turn. An attacker with write access to the project's working directory could craft Makefile content designed to manipulate the agent's behavior.
Preconditions: Attacker has write access to the Makefile in the agent's working directory. The agent must be running in that directory (local mode, not sandbox mode).

T8: Custom Subagent Body Used as System Prompt Without Validation

Flow: DF21
Description: subagents._parse_subagent_file reads AGENTS.md files from .deepagents/agents/{name}/AGENTS.md and project-level .agents/{name}/AGENTS.md. The markdown body after the YAML frontmatter is used verbatim as the subagent's system_prompt. No content filtering is applied.
Preconditions: Attacker has write access to ~/.deepagents/agents/ or the project's .agents/ directory. User or LLM must invoke the malicious subagent via the task tool.

T9: Arbitrary Python Code Execution via `class_path` Config

Flow: DF23 (config.toml → importlib)
Description: The class_path field in [models.providers.<name>] config triggers importlib.import_module() in config._create_model_from_class. While the imported class is validated as a BaseChatModel subclass, module-level code executes unconditionally during import — before the type check runs. A malicious or compromised config.toml pointing to a hostile module causes arbitrary code execution at model initialization time. This applies to both class_path and the _load_provider_profiles path that uses exec_module() to load _profiles.py from provider packages.
Preconditions: Attacker has write access to ~/.deepagents/config.toml AND a malicious Python package installed in the user's environment (or on sys.path). The code comments document this as intentional: "same trust model as pyproject.toml build scripts — the user controls their own machine."

T10: MCP Stdio Env Dict Forwarded Without Filtering

Flow: DF24 (MCP config → subprocess environment)
Description: The "env" field in stdio MCP server definitions (.mcp.json) accepts an arbitrary key-value dict. mcp_tools._validate_server_config only checks that the field is a dict — it does not filter key names or values. The dict is forwarded directly to StdioConnection(env=...) which passes it to the subprocess. An attacker who can modify a project-level .mcp.json could set PATH to redirect command resolution, LD_PRELOAD to inject shared libraries, or PYTHONPATH to hijack Python imports in the MCP subprocess.
Preconditions: (1) Attacker has write access to a project-level .mcp.json; (2) The project MCP config must be approved by the user (the interactive prompt, --trust-project-mcp, or the server approved via [mcp].enabled_project_server_approvals / the DEEPAGENTS_CODE_DANGEROUSLY_ENABLE_PROJECT_MCP_SERVERS env var). For user-level ~/.deepagents/.mcp.json, the attacker already has home directory write access. Note: the env dict from MCP config is passed to StdioConnection — whether it replaces or merges with os.environ depends on the langchain_mcp_adapters library implementation.

T11: Auto-Installed ripgrep Binary from Upstream Release

Flow: Download performed by managed_tools.ensure_ripgrep when rg is not on PATH — either on first run, or eagerly at install time via dcode tools install (invoked by scripts/install.sh).
Description: Without a system rg, Deep Agents Code fetches the pinned ripgrep release tarball from github.com/BurntSushi/ripgrep/releases/..., verifies it against an in-tree SHA-256 (RIPGREP_ASSETS), extracts it under a TemporaryDirectory, and atomically moves the binary into managed_tools.BIN_DIR (<sys.prefix>/share/deepagents-code/bin/rg, shared by every profile), or into the profile-scoped managed_tools.FALLBACK_BIN_DIR when that directory is not writable. The binary then runs unsandboxed, inheriting the same trust as a user-installed rg (the SDK invokes it via subprocess.run(["rg", ...])). The same verified path backs the dcode tools install verb, so the install script reuses it rather than re-encoding the version + checksum table in bash.
Mitigations: (1) SHA-256 verified against the pinned hash table before move — a mismatch aborts the install and leaves the chosen bin directory clean. (2) Network egress is limited to github.com. (3) Opt-out via DEEPAGENTS_CODE_OFFLINE for air-gapped environments, or DEEPAGENTS_CODE_RIPGREP_INSTALLER=system to defer to the OS package manager instead of the managed binary. (4) Pinned version + checksums are bumped in-tree, so a compromised upstream release is detected on the next Deep Agents Code release rather than silently propagating. (5) Atomic move-into-place avoids partial installs when concurrent CLI invocations race. (6) The eager install-script path is non-sudo (no system package manager is invoked in the default managed mode).
Preconditions: User has not installed rg via their package manager, DEEPAGENTS_CODE_OFFLINE is unset, DEEPAGENTS_CODE_RIPGREP_INSTALLER is not system, and the host can reach github.com. The pinned SHA-256 in RIPGREP_ASSETS would need to be incorrect (a supply-chain compromise of the deepagents-code release) for a tampered binary to be installed.

T11b: Unpinned Pricing Catalog Fetched Hourly from a Mutable Upstream Ref

Flow: Background daemon thread started by cost_tracking._start_price_updater on the first priced model request.
Description: Unless opted out, Deep Agents Code starts genai_prices.UpdatePrices, which fetches raw.githubusercontent.com/pydantic/genai-prices/refs/heads/main/prices/new_data/v2/data.json every hour and installs it via set_custom_snapshot. The fetched catalog wholesale-replaces the pricing data bundled with the installed package for the life of the process. Unlike the ripgrep download (T11), the payload is not checksummed and the URL names a mutable branch ref rather than a pinned release, so the content can change between any two fetches. The blast radius is confined to displayed cost estimates — the catalog is parsed as data by genai-prices, never executed — but corrupt, regressed, or hostile upstream data silently changes every cost figure the user sees, and a catalog that omits providers makes lookups fail in a way that reads as "this model has no published rates."
Mitigations: (1) Opt-out via DEEPAGENTS_CODE_PRICES_AUTO_UPDATE=0 or [update].prices_auto_update = false in config.toml, or DEEPAGENTS_CODE_OFFLINE for air-gapped environments, all checked before the thread starts. (2) cost_tracking._build_price_updater refuses a fetched catalog listing fewer providers than the bundled one, so a truncated or mid-publish data.json cannot take effect. (3) A refused or failed fetch leaves the previously installed catalog in place rather than clearing it. (4) genai-prices rejects any payload that is not a JSON array of schema-valid providers. (5) Network egress is limited to raw.githubusercontent.com. (6) The updater is started lazily on first pricing, never at CLI startup, so a session that prices nothing makes no request.
Preconditions: DEEPAGENTS_CODE_PRICES_AUTO_UPDATE is not falsy, DEEPAGENTS_CODE_OFFLINE is unset, the host can reach raw.githubusercontent.com, and at least one model request is priced. For tampered data to be installed, the upstream repository or the CDN path would need to be compromised and the substituted catalog would need to list at least as many providers as the bundled data.

T12: Project `.env` Injects Shell Interpreter Startup Hooks

Flow: DF10 (project .env → process environment) → bash subprocess startup (DF19)
Description: config._load_dotenv discovers the nearest project .env by walking up from the working directory and applies its values to the process environment (override=False, so shell-exported values still win). Bash treats several environment variables as startup hooks — BASH_ENV and ENV name a file that is sourced when a non-interactive shell starts. Because Deep Agents Code runs its own local-context detection through Bash at startup (local_context.build_detect_script) and spawns shells for the execute tool, a project .env that sets one of these keys could run attacker-controlled scripts inside the dcode process before any model-requested tool call or HITL approval prompt. The broader trust boundary is that running dcode in a project directory lets that project influence the process environment. The mitigation is a key denylist (config._DOTENV_DENIED_ENV_KEYS) covering known execution-hook consumers (shell startup, dynamic linker, interpreter startup/paths, askpass hijack); it is a best-effort enumeration, not a closed set, so an execution hook consumed by some other spawned tool remains viable until its keys are added to the list.
Preconditions: (1) User launches dcode in a directory containing an attacker-controlled .env (for example, a freshly cloned untrusted repository); (2) the .env sets a shell startup-hook key that is not already exported in the user's shell (dotenv uses override=False).

T13: First-Token Shell Allow-List Bypass via Interpreters/Wrappers

Flow: DF7 (LLM tool call) → C4 Tools (execute)
Description: is_shell_command_allowed splits a command on |, ;, &&, and ||, then validates only the first token (tokens[0]) of each segment against the configured allow-list, after rejecting a set of dangerous patterns (contains_dangerous_patterns: command substitution $(/backticks, redirects, ${, bare $VAR, background &). Because only the executable name is checked, any allow-listed general-purpose interpreter or command wrapper carries arbitrary behavior in its arguments: python3 -c '<code>', bash script.sh, sh -c '<code>', env <cmd>, xargs <cmd>, uv run <cmd>, and similar. A user who allow-lists such a program — reasonably believing they have restricted the agent to a "safe" tool — has effectively allowed unrestricted execution, and in headless -x/Auto mode those commands run without an approval prompt. This is the same class of weakness as T2 but applies to normal, non-all allow-lists. The allow-list is an ergonomic auto-approve heuristic, not a security boundary; textual command classification cannot robustly constrain execution.
Preconditions: (1) User configures a shell allow-list (--shell-allow-list / DEEPAGENTS_SHELL_ALLOW_LIST) that includes an interpreter or wrapper; (2) non-interactive or Auto mode (interactive Manual mode still shows the human the literal command before it runs); (3) for the injected-command variant, successful prompt injection via DF8/DF9. The intended control is HITL approval; OS-native execution sandboxing would be the robust boundary the string allow-list cannot provide.

T14: Weaker Auto Classifier Model Weakens Action Review

Flow: DF7 (LLM tool call) → Auto classifier review → C4 Tools
Description: In Auto approval mode, gated tool calls that deterministic policy cannot clear are reviewed by an LLM authorization classifier. That classifier can be pointed at a separate model (--auto-classifier-model, DEEPAGENTS_CODE_AUTO_CLASSIFIER_MODEL, [models].auto_classifier, or /auto model) so reviews are cheaper and faster than the main agent model. Review quality then follows the chosen model: a weaker one is likelier to mis-authorize an action, and likelier to be steered by injected instructions in the untrusted material it reads (tool arguments, paths, prior tool output, remote metadata, and the model-authored ask_user question text paired with a same-turn answer). Question text is a distinct case: the ask_user receipt attests only that this exact text was displayed to the user and answered, never that its content is true, so a question asserting prior or blanket authorization is untrusted content rather than evidence. _CLASSIFIER_POLICY instructs the classifier to read a paired question strictly as a description of a proposed action and target, to disregard directives embedded in it, and to allow an action only where that description matches the action's canonical arguments. Choosing the classifier is a user-level decision, so it is restricted to trusted surfaces: shell exports, the global ~/.deepagents/.env, ~/.deepagents/config.toml, the CLI flag, and /auto model. A project .env travels with a cloned repo, so DEEPAGENTS_CODE_AUTO_CLASSIFIER_MODEL is listed in config._PROJECT_DOTENV_DENIED_ENV_KEYS — the same mitigation TB4 applies to the project-MCP trust vars; without that entry a checked-in .env could silently downgrade the review on dcode startup. With it, this is a self-inflicted weakening of a control rather than an external attack path. Auto's model-independent guards are unchanged by the setting: deterministic allow (the deterministic deny rules are narrow and do not cover the Deny categories, so an action the classifier affirmatively allows is not re-checked downstream), the consecutive/total denial counters, batch-replay detection, control-state availability, and the human-fallback thresholds. A classifier model that cannot be constructed (bad spec, missing credentials, uninstalled provider package) never falls back to the main model — the first such batch is marked classifier_unavailable, so those calls are denied and do not execute, and the failing spec is latched so every subsequent batch escalates straight to human approval until a review succeeds. A transient failure instead escalates once _CONSECUTIVE_UNAVAILABLE_FALLBACK consecutive batches have failed, or immediately when Auto's control state cannot be persisted. The review deadline is tunable through a subset of the same trusted surfaces — shell exports, the global ~/.deepagents/.env, and [models].auto_classifier_timeout in ~/.deepagents/config.toml (there is no CLI flag and no /auto subcommand for it) — and DEEPAGENTS_CODE_AUTO_CLASSIFIER_TIMEOUT is denied from a project .env for the same reason, so a cloned repo cannot stall gated batches or squeeze the budget until reviews time out. config_manifest.resolve_auto_classifier_timeout rejects any resolved value outside [AUTO_CLASSIFIER_TIMEOUT_FLOOR, AUTO_CLASSIFIER_TIMEOUT_CEILING] — out-of-range and malformed values are discarded in favor of the next config source rather than clamped — so the deadline itself cannot be removed; a timed-out batch remains fail-closed (classifier_unavailable).
Preconditions: (1) Auto approval mode is active (interactive TUI only, and not under a sandbox); (2) a classifier model is configured through one of the trusted surfaces above — the default reuses the main agent model; (3) for the injected-review variant, untrusted content reaches the classifier through DF8/DF9 and the chosen model follows it. The intended control is that the human remains the fallback for anything the classifier does not affirmatively allow.

Input Source Coverage

Input Source	Data Flows	Threats	Validation Points	Responsibility	Gaps
User direct input	DF1, DF2	None (TB1)	None — prompts accepted verbatim	User	No content filtering — intentional; HITL gates downstream tool calls
LLM output	DF6, DF7	T1, T2, T3, T4, T13, T14	HITL gate; shell allow-list; Unicode/URL warnings on tool args; Auto classifier review	Project	LLM-generated tool args not scanned for injection beyond Unicode/URL; shell allow-list matches only the command's first token, so allow-listed interpreters/wrappers bypass it (T13); Auto classifier review quality follows the user-selected classifier model, and the classifier reads untrusted tool arguments, prior output, and model-authored `ask_user` question text (T14)
Tool/function results	DF9, DF11	T1	Unicode warning on URL args; `markdownify` HTML conversion	Shared	Tool results pass to context without prompt-injection scan
URL-fetched content	DF8, DF9	T1	`check_url_safety` on URL arg; HTML→markdown conversion	Shared	Markup-embedded instructions survive markdownify; no LLM-layer guardrail
Configuration	DF10, DF12, DF15, DF23	T9, T10, T12, T14	Dotenv shell-env precedence; TOML schema; MCP schema + allow/deny lists; JSON structure check; `class_path` format check; dotenv denylist for execution-hook env keys and project-`.env` trust vars	User	Dotenv denylist is best-effort — execution-hook env keys consumed by tools not yet enumerated still reach subprocesses; `class_path` executes module code before type check; MCP env dict unfiltered; Auto classifier strength is a user choice with no floor enforced (T14)
Session restore	DF14	T5	OS file permissions; SQLite	Project	Unencrypted at rest
Server IPC (env vars)	DF18	T6	`ServerConfig` serialization; parent env passed to child	Project	Provider API keys flow to server subprocess; system prompt in env
Offload operation	DF26, DF27	T6	Per-field request schema on consumed context keys; endpoint/transport keys stripped from client `model_params` (`offload_api._strip_transport_model_params`); idle/pending/checkpoint checks; state-only update typed to permitted channels; messages writes rejected	Project	`context.model`/`profile_overrides` are type-checked but their values flow to `config.create_model` (see C17/TB11 for `class_path`); unknown context keys pass through by design; local built-in route relies on loopback and `noop` auth; custom deployments own route auth and thread authorization
Host environment	DF19, DF20	T7	Static script; exit code check; 30s timeout	Shared	Makefile content injected into system prompt without sanitization
Custom subagents (FS)	DF21	T8	`yaml.safe_load`; HITL on `task` tool	User	Subagent body text not content-filtered
Async subagent config	DF22	None direct	TOML parse; type validation in `load_async_subagents`	User	URL and headers for remote subagents are user-controlled; no URL validation
MCP subprocess env	DF24	T10	Dict type check only (`_validate_server_config`)	User	No key/value filtering; arbitrary env vars forwarded to subprocess
Offloaded history	DF25	None direct	Filename is a framework-minted session id (no path injection); backend handles storage	Shared	Raw conversation content written to sandbox filesystem
Goal/rubric state	DF28, DF29	T15, T16, T17	Lifecycle projection; notice fingerprinting; raw-character validation (8,000 objective; 12,000 rubric and objective-plus-criteria; 4,000 note/blocker; 16,000 notice); boundary-tag escaping	Shared	Untrusted instructions remain model-readable; file contents are automatically transmitted; no post-escape, byte, or token budget or provider-transmission warning

Out-of-Scope Threats

Threats that appear valid in isolation but fall outside project responsibility because they depend on conditions the project does not control.

Pattern	Why Out of Scope	Project Responsibility Ends At
Prompt injection leading to arbitrary code execution (interactive mode)	In interactive mode, every side-effecting tool call requires explicit user approval via the HITL dialog. The user is the final gatekeeper.	Providing HITL for all side-effecting tools (`agent._add_interrupt_on`) and Unicode/URL warnings in the approval dialog.
API key exfiltration via LLM-directed `http_request`	`http_request` requires HITL in interactive mode. Keys it could exfiltrate are user-supplied env vars. In non-interactive mode, user has opted into autonomous operation.	Providing HITL gate for HTTP tools. Users control which env vars are in scope.
Malicious MCP server injecting prompt instructions	Users configure MCP servers and explicitly trust project-level configs. Once trusted, MCP tool outputs are data from a system the user controls.	Interactive approval prompt + per-server allow/deny lists for project-level configs (`main._check_mcp_project_trust`, `model_config.load_mcp_server_trust_lists`).
LLM jailbreak / safety bypass	Model selection and safety configuration are user-controlled. The project routes prompts to the configured LLM but cannot guarantee model behavior.	Correctly routing prompts to the configured LLM; applying the system prompt from `agent.get_system_prompt`.
Sandbox provider security vulnerabilities	Daytona, LangSmith, Modal, Runloop, and AgentCore are third-party services. Their internal security is not this project's responsibility.	Correctly initializing sandbox sessions via `integrations.sandbox_factory.create_sandbox`.
Hook commands doing harmful things	User-scoped hooks (`~/.deepagents/hooks.json`), project-scoped hooks (`.deepagents/hooks.json`, only after interactive workspace trust or `--trust-project-hooks`), and plugin-scoped hooks (`hooks/hooks.json` in a plugin the user installed and enabled) are intentionally configured commands. The payload is data-only (JSON on stdin).	Schema validation (`hooks.loading.load_hooks_config`); workspace trust for project hooks (versioned store under `~/.deepagents/.state/hooks_trust.json`; cancelling the trust prompt aborts startup); install plus enablement for plugin hooks, with declared events listed in the plugin manager; bounded execution with per-event default timeouts (600s for most events, 30s for `UserPromptSubmit`); sanitized subprocess environment with only the plugin's own path variables overlaid.
Async subagent traffic interception / MitM	Async subagents connect to user-configured LangGraph deployment URLs. The project does not control those endpoints or their TLS certificates.	Accepting URL/headers from user config and passing them to the LangGraph SDK (`agent.load_async_subagents`).
LangGraph dev server port enumeration / discovery	Discovering the local dev server port requires local access. Port scanning localhost is a general OS security concern, not a framework vulnerability.	Binding to `127.0.0.1` by default (`server._DEFAULT_HOST`); ephemeral server lifetime; OS-assigned ephemeral port (`server._EPHEMERAL_PORT`) is not predictable across runs.
`.env` file from parent directory changes app/API configuration	`config._find_dotenv_from_start_path` walks up the directory tree to find `.env` files. Discovering ordinary configuration values (API keys, `DEEPAGENTS_CODE_` settings) this way is standard `python-dotenv` behavior, and the user controls their filesystem. The code-execution* implication of a project `.env` (shell startup hooks) is tracked in-scope as T12.	Finding `.env` from the project root (`config._find_dotenv_from_start_path`); `override=False` by default (existing env vars preserved); shell startup / environment-hijack keys (`BASH_ENV`, `ENV`) denied during dotenv loading.

Rationale

Prompt injection in interactive mode: The HITL interrupt means every file write, shell command, web search, URL fetch, task delegation, and async subagent action shows the user a confirmation dialog with full tool arguments. Even a successful prompt injection can only execute what the user explicitly approves. The project's responsibility is to make that dialog accurate — hence the Unicode/URL warning layer in unicode_security.py.

LangGraph dev server without auth: The LANGGRAPH_AUTH_TYPE=noop setting is intentional for local dev server use. Adding authentication would require users to manage tokens for a locally-spawned ephemeral process, creating more friction than security benefit in this context. The 127.0.0.1 binding limits exposure to the local machine. T6 documents this as an accepted risk for the threat model.

Custom subagent system prompts: Subagent definitions in .deepagents/agents/ are user-authored files. The framework correctly treats them as user-controlled content. The HITL gate on task tool calls ensures the user approves subagent delegation before it occurs.

class_path code execution: This follows the same trust model as pyproject.toml build scripts — the user edits their own config file on their own machine. The issubclass(BaseChatModel) check provides a post-import guard, though module-level side effects execute before it. Documented as intentional in model_config.py.

Open Questions

Should the raw-character limits for goal objectives, rubric criteria, status notes, and complete notices instead account for HTML-escaped/rendered size or provider tokenization and available context? This is required to resolve the residual risk in T17.
Should /rubric file display a provider-disclosure warning or require confirmation before its full contents are persisted and sent to the configured model provider? This determines the intended handling for T16.
Is automatic injection permitted only for content the user intentionally designates as agent-control input, or may it include arbitrary repository-file content? This trust contract determines whether T15 is an accepted product risk or needs a different retrieval design.
What retention, deletion, geographic-processing, and logging commitments does each configured model provider make for goal/rubric notices? The model provider and its credentials are user-configured; those facts are not established by the scoped code.

Investigated and Dismissed

ID	Original Threat	Investigation	Evidence	Conclusion
D1	Unsafe msgpack deserialization in langgraph checkpoint loading	Verified fix status — confirmed fixed and closed upstream.	Users on current `langgraph` versions are not exposed.	Upstream langgraph has patched the unsafe msgpack deserialization. No longer an active risk.
D2	Unicode URL homoglyph as project vulnerability	Traced `check_url_safety` + `strip_dangerous_unicode` + `format_warning_detail` → approval dialog display	`unicode_security.check_url_safety`, `agent._format_fetch_url_description`	Warning system is the intended control — the project correctly surfaces the risk to the user in the approval dialog. Not a project vulnerability; classified as mitigated by design (UI warning).
D3	SSRF via `http_request` / `fetch_url` to internal services	Traced `tools.http_request` and `tools.fetch_url` — no URL scheme or host blocklist. However, both tools require HITL approval in interactive mode. In non-interactive mode, only shell commands are auto-approved via the allow-list; HTTP tools still go through the HITL interrupt gate.	`tools.http_request`, `tools.fetch_url`, `agent._add_interrupt_on`	Not a project vulnerability in isolation — the HITL gate is the intended control for all HTTP tool calls. The user sees the full URL before approving. SSRF is only reachable if the user approves the request (interactive) or enables auto-approve (explicit opt-in). Classified as out-of-scope for the same reason as prompt injection in interactive mode.
D4	Offload path injection via archive filename	Checked `SummarizationMiddleware._get_session_id`/`_get_history_path` — the leaf is `session_` plus a `uuid4().hex`.	`SummarizationMiddleware._get_session_id`, `SummarizationMiddleware._get_history_path`	The archive filename is minted by the framework from a UUID4 hex, so no user-controlled path component reaches the file path. Not exploitable.

Revision History

Date	Author	Changes
2026-03-10	langster-threat-model (automated)	Initial threat model
2026-03-27	langster-threat-model (automated)	Deep expansion: added C11-C16 (server subprocess, RemoteAgent, LocalContextMiddleware, async subagent config, custom subagent loader); added TB8-TB10 (CLI/server IPC, LocalContext/host env, RemoteAgent/dev server); added DF18-DF22; added T6 (unauthenticated dev server), T7 (Makefile injection), T8 (subagent body injection); updated T5 (upstream msgpack fix confirmed); added data classification; added Investigated and Dismissed section; updated architecture diagram to reflect server-subprocess model
2026-03-28	langster-threat-model (automated)	Deep validation pass: added C17 (Model Config Loader with class_path), TB11 (Config→Code Execution); added DC5 (offloaded conversation history); added DF23-DF25 (class_path flow, MCP env dict flow, offload flow); added T9 (class_path arbitrary code execution), T10 (MCP env dict unfiltered); added D3 (SSRF dismissed — HITL is intended control), D4 (offload path injection dismissed — UUID7); removed Status column and Mitigations/Residual Risk fields from all threats (open source visibility compliance — mitigation status must not appear in public threat models); updated T6 validation from Likely to Verified (port is deterministic at 2024 default, discoverable via /proc); updated external context (no published advisories found); updated architecture diagram
2026-06-25	manual update	Added T12 (project `.env` injects shell interpreter startup hooks) under TB11; extended the TB11 boundary controls and details with the dotenv shell-startup-hook denylist (`BASH_ENV`, `ENV`); clarified the `.env` out-of-scope row to separate ordinary config discovery from the in-scope code-execution implication
2026-06-25	manual update	Updated T6 and TB10 to reflect the dev server binding an OS-assigned ephemeral port by default (`server._EPHEMERAL_PORT`) instead of squatting the well-known `langgraph dev` port 2024; noted unpredictable-port-across-runs as added defense-in-depth in the port-discovery dismissal row
2026-07-08	manual update	Removed the SHA-256 config fingerprint trust store (`mcp_trust.py`, `~/.deepagents/.state/mcp_trust.json`, DC4). Project MCP trust is now the interactive approval prompt (allow-for-session / always-allow scoped to project root + server-definition fingerprint / deny), the `--trust-project-mcp` run flag, the `[mcp].enabled_project_server_approvals` and `[mcp].disabled_project_servers` lists, and the process-wide `DEEPAGENTS_CODE_DANGEROUSLY_ENABLE_PROJECT_MCP_SERVERS` name-based escape hatch. The legacy flat `[mcp].enabled_project_servers` key is ignored. Persisted approvals bind to a server definition's fingerprint rather than a whole-config fingerprint. Updated C5, TB4, T10, the configuration input-coverage row, and the malicious-MCP-server dismissal accordingly
2026-07-21	manual update	Clarified TB4 after process-wide MCP names and scoped remembered approvals changed from replacement semantics to independent grants. An empty process-wide allowlist no longer suppresses remembered approvals; disabled-server precedence is unchanged.
2026-07-22	manual update	Added T13 (first-token shell allow-list bypass via allow-listed interpreters/wrappers) under TB2, distinguishing it from T2's `--shell-allow-list all` sentinel; cross-referenced it from T2 and extended the "LLM output" input-coverage gap to note that allow-list matching only inspects the command's first token
2026-07-24	manual update	Corrected the out-of-scope hooks row: Hooks v2 defaults are 600s (30s for `UserPromptSubmit`), not a global 5-second timeout; loading is `hooks.loading.load_hooks_config`; project hooks require workspace trust or `--trust-project-hooks`
2026-07-28	manual update	Extended the out-of-scope hooks row for plugin-contributed hooks: enabled plugins may supply `hooks/hooks.json`, gated by install plus enablement rather than workspace trust, with each handler's environment overlaid only by its own plugin path variables
2026-08-03	manual update	Added T14 (a weaker Auto classifier model weakens action review) under TB2, covering the selectable classifier (`--auto-classifier-model`, `DEEPAGENTS_CODE_AUTO_CLASSIFIER_MODEL`, `[models].auto_classifier`, `/auto model`), its restriction to trusted config surfaces via `config._PROJECT_DOTENV_DENIED_ENV_KEYS`, and its fail-closed construction behavior (deny, then latch to human approval; never fall back to the main model). Extended the "LLM output" and "Configuration" input-coverage rows with T14
2026-08-04	manual update	Extended T14 for the configurable Auto classifier review deadline (`DEEPAGENTS_CODE_AUTO_CLASSIFIER_TIMEOUT`, `[models].auto_classifier_timeout`): bounded by `config_manifest.resolve_auto_classifier_timeout` between a floor and ceiling so the deadline cannot be removed, denied from a project `.env` via `config._PROJECT_DOTENV_DENIED_ENV_KEYS`, and fail-closed on expiry
2026-08-11	langster-threat-model (automated)	Added C19, TB12, and DF28/DF29 for persisted goal/rubric state injected into primary-model context after removal of the goal/rubric read tools. Updated DC2 and input-source coverage; added T15 (stored prompt injection), T16 (automatic disclosure of file-loaded criteria), and T17 (context-budget pressure), with provider-handling and trust-contract gaps recorded as Open Questions.
2026-08-11	langster-threat-model (automated)	Corrected TB12, DF28/DF29, and T17 to document the enforced raw-character limits and the narrower residual risk from post-escape expansion and provider-specific byte/token budgets.
2026-08-17	langster-threat-model (diff)	Added C18, a server-owned custom HTTP boundary, and updated the architecture, DC5, TB2, TB10, DF25-DF27, T6, and input coverage. The route owns checkpoint hydration, shared compaction/hooks/backend selection, state-only persistence typed to permitted channels, and cost rollback; the client sends no graph or checkpoint state. Recorded that `PreToolUse` `ask` is fail-closed (converted to a deny) on this path because the operation transport carries no HITL channel; no new threat was identified.
2026-08-17	manual update	Noted that the goal-state character budget is re-validated when a one-shot `/rubric next` is consumed, not only when it is set: the goal state it is measured against is mutable between those points (`/goal amend`, an `update_goal` blocker note), so a set-time-only check could still degrade the notice and silently disable the promised grade. The degraded notice now also reports `Goal status: unavailable` rather than leaving a live status beside `Goal actionable: no`.
2026-08-17	manual update	Extended T14 for `ask_user` question text as a classifier injection source: the receipt attests display and answer, not content, so a question claiming prior or blanket authorization is untrusted content; recorded the `_CLASSIFIER_POLICY` clauses that keep a paired question to an action/target description matched against canonical arguments. Narrowed the T14 "deterministic allow/deny" guard wording, which overstated the deny side: deterministic denies do not cover the Deny categories, and an affirmative classifier allow is not re-checked downstream
2026-08-19	manual update	Recorded that a superseded goal-state notice is replaced in place rather than removed from a model request, because the summarizer derives its next cutoff from that list and persists it against the unfiltered checkpoint; and that a combined objective-plus-criteria overflow now ends the criteria turn with its character limit instead of retrying blind to the recursion limit. Noted that an unrecognized persisted goal status degrades to `paused` in the notice, so a corrupt or forward-version checkpoint cannot present itself to the model as a goal to work toward. Dropped the stale generated-commit pin and bounded T16's disclosure to the enforced rubric limit
2026-08-21	manual update	Clarified that the dotenv execution-hook denylist (`config._DOTENV_DENIED_ENV_KEYS`) is a best-effort enumeration of known code-execution consumers, not a closed set: TB11's inside-detail and T12's description now state this explicitly, and the Configuration input-coverage gap was reworded from the stale "project `.env` can set shell startup-hook vars" (denylisted since #4288) to the actual residual — execution-hook keys consumed by not-yet-enumerated tools still reach subprocesses
2026-08-24	langster-threat-model (diff)	Removed the client-seeded `/offload` fallback. `/offload` is now available only through C18 on built-in servers; local in-process and ACP agents do not support it, and custom or older servers without the route fail at the HTTP boundary. Updated DC5, TB2, TB10, and T6 to remove the client self-approval and synthetic-message attack surface. The server route, hook behavior, archive guard, and state-only persistence controls are unchanged; no new threat was identified.
2026-08-24	manual update	The C18 boundary now strips endpoint/proxy/transport keys (`base_url`, `openai_proxy`, `http_client`, and similar) from client-supplied `model_params` before they reach `config.create_model` (`offload_api._strip_transport_model_params`), closing the credential-redirection consequence of T6 for this route. Client-supplied `model` and behavioral params still flow through; in-process `CLIContextSchema` model params remain trusted and unfiltered
2026-08-25	manual update	Stopped replacing bounded superseded goal-state notices in model requests while retaining bounded same-index stand-ins for oversized legacy notices. Goal/rubric history now remains append-only for prompt-cache stability where safe, and the latest notice explicitly supersedes earlier notices. Updated T15 to record the residual risk that a model can still attend to older bounded goal text until compaction.

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