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* feat(sandbox): per-call env injection + platform-secret scrubbing for skills Add an env parameter to Sandbox.execute_command (abstract + local + AIO) so request-scoped secrets can be injected into skill subprocesses, and scrub platform credentials (*KEY*/*SECRET*/*TOKEN*/*PASSWORD*/*CREDENTIAL*) from the inherited environment by default so scoped injection is not security theatre. LocalSandbox always passes an explicit scrubbed env; AioSandbox routes env-bearing commands through bash.exec(env=) on a fresh session and leaves the legacy persistent-shell path unchanged. Part of #3861. BEHAVIOR CHANGE: execute_command no longer inherits the full os.environ; Windows encoding tests updated to assert the scrubbed dict. * feat(skills): parse required-secrets frontmatter declaration Add SecretRequirement and Skill.required_secrets, and parse the required-secrets SKILL.md frontmatter field (a string list or {name, optional} mappings), dropping malformed entries with a warning so one bad declaration does not invalidate the skill. The declared name is both the context.secrets key and the env var injected at activation. Part of #3861. * feat(runtime): request-scoped secret carrier (context.secrets) Add SECRETS_CONTEXT_KEY + extract_request_secrets, centralising the context.secrets carrier contract. The existing context passthrough (build_run_config -> _build_runtime_context) already carries the sub-key to runtime.context without mirroring it into configurable; characterization tests lock that behaviour. Part of #3861. * feat(skills): inject declared secrets at slash-activation into bash env Binding point A: when a skill is slash-activated, SkillActivationMiddleware resolves its declared required-secrets against the request's context.secrets and writes the per-run injection set to runtime.context. The bash tool forwards that set to execute_command(env=). A skill cannot harvest a host platform credential (is_host_platform_secret guard, cf. GHSA-rhgp-j443-p4rf), and injected values are redacted from bash output (mask_secret_values) so an echoed secret never re-enters the prompt/trace. Part of #3861. * test(skills): lock the five secret leak surfaces + add trace redaction helper Regression tests assert the secret value is absent from all five surfaces: prompt (activation message), checkpoint (graph state vs context separation), audit (journal records names only), trace (metadata builder never copies context; never mirrored to configurable), and stdout (mask_secret_values). Add redact_secret_context_keys as a defensive helper for any context serialization. Part of #3861. * docs(backend): document request-scoped secrets for skills Add Request-Scoped Secrets subsection (Skills) + env policy note (Sandbox) and the execute_command(env=) signature change, per the doc-sync policy. Part of #3861. * fix(skills): close gaps found by end-to-end verification of request-scoped secrets Real-gateway e2e + independent review of #3861 surfaced three defects, now fixed: 1. Slash activation never fired in the live chain. InputSanitizationMiddleware wraps user input in BEGIN/END markers before SkillActivationMiddleware sees it, and the original text was only preserved when an upload or IM channel set it. For a plain text message the slash command became undetectable, so no secret was ever resolved. Fix: the sanitizer now setdefaults the pre-wrap text into ORIGINAL_USER_CONTENT_KEY (additive; sanitization behaviour unchanged), so slash activation works for all messages. Pre-existing latent bug surfaced here. 2. The raw request config (with context.secrets) was persisted to runs.kwargs_json and echoed by the run API (RunResponse.kwargs). Fix: redact_config_secrets() strips secret-bearing context keys from the persisted/echoed copy in start_run; the live config that drives the run keeps them. build_run_config now also sets configurable.thread_id on the context path (the checkpointer requires it). 3. Connection-string credentials (DATABASE_URL, REDIS_URL, SENTRY_DSN, GH_PAT, ...) were not scrubbed from the inherited sandbox env. Fix: env_policy adds a *DSN* pattern plus an explicit connection-string denylist (no blanket *URL* — benign service URLs stay readable). Verified end-to-end via a real gateway run (real LLM + skill activation + bash): the secret reaches the sandbox subprocess and appears in NONE of prompt, trace, checkpoint, audit, stdout, runs.kwargs_json, or the run API. Part of #3861. * docs(backend): document the env scrub, persistence redaction, and sanitizer interaction Sync the Request-Scoped Secrets section with the verification-driven fixes: inherited-env scrub (incl. connection-string denylist), run-record/run-API redaction as the 6th sealed leak surface, and the sanitizer preserving original content so slash activation fires. Part of #3861. * fix(skills): inject caller secret over scrubbed host value; drop redundant host-name guard A real-world demo (a skill calling a third-party cloud API with a request-scoped key) exposed that the is_host_platform_secret guard was both wrong and harmful: it refused to inject a caller-supplied secret whenever a same-named variable existed in the Gateway env — which is exactly the #3861 use case (a per-user key overriding a shared platform key). The guard was also redundant: build_sandbox_env already scrubs secret-looking names from the inherited env before injection, so a skill can never read a host credential — it only ever receives the caller's value. Remove the guard; the injected (caller) value simply wins over the scrubbed host value. Verified end-to-end: the agent called the real cloud API successfully with the caller's key, the host's same-named key was scrubbed and never used, and the caller's key leaked to none of the surfaces. Part of #3861. * fix(skills): address review on request-scoped secrets (#3861) Review fixes from PR #3871: - E2BSandbox.execute_command now accepts env/timeout and routes them to commands.run(envs=, timeout=). The bash tool passes env= unconditionally, so the prior signature (command only) raised TypeError on every e2b bash call and broke e2b deployments entirely. env=None stays backward-compatible. - SkillActivationMiddleware clears the active-secret set before resolving each activation, so a later skill in the same run never inherits an earlier skill's injection set (the #3861 contract: a skill only receives what the caller supplied AND that skill declared). - AioSandbox env path uses a dedicated _DEFAULT_HARD_TIMEOUT — bash.exec exposes no idle/no-change timeout, so the prior reuse of the legacy idle constant conflated wall-clock vs idle semantics. The env path also retries on the ErrorObservation signature now, sharing the legacy persistent-shell recovery contract. - mask_secret_values skips values below a minimum length floor so a short declared secret (e.g. "42") cannot shred unrelated bytes (exit codes, timestamps, sizes) of tool output. The secret is still injected into the subprocess; only the output mask skips it. session_id reuse on the env path is intentionally NOT added: a shared session could let request-scoped secrets ride the session env into later commands, which the SDK does not contractually forbid. The fresh-session choice matches the LocalSandbox model (each call is a fresh subprocess); the trade-off (consecutive env-bearing calls do not share cwd/venv/exports) is documented on _execute_with_env.
289 lines
11 KiB
Python
289 lines
11 KiB
Python
"""Input guardrail middleware for prompt-injection defense (issue #3630).
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Escapes blocked XML-like tags in the last genuine user message (e.g.
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``<system>`` → ``<system>``) so they render as literal text instead
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of structured-context markers. This preserves the user's intent ("how do
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I use DeerFlow's <think> tag?") while neutralizing injection attempts —
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the same de-identify-don't-reject strategy as AWS Bedrock's PII ANONYMIZE.
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Blocked: system-reserved tags (memory, analysis, etc.) + common injection
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tags (system, instruction, role, etc.). Normal HTML/XML tags (<div>,
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<span>) are NOT escaped.
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Clean input is wrapped in plain-text boundary markers as a secondary
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semantic defense (OWASP structured-prompt guidance).
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"""
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from __future__ import annotations
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import logging
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import re
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from collections.abc import Awaitable, Callable
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from typing import override
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from langchain.agents import AgentState
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from langchain.agents.middleware import AgentMiddleware
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from langchain.agents.middleware.types import (
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ModelCallResult,
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ModelRequest,
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ModelResponse,
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)
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from langchain_core.messages import HumanMessage
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from langgraph.errors import GraphBubbleUp
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from deerflow.utils.messages import ORIGINAL_USER_CONTENT_KEY, message_content_to_text
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logger = logging.getLogger(__name__)
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_SUMMARY_MESSAGE_NAME = "summary"
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# Finite set of blocked tag names: system-reserved + common injection patterns.
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_BLOCKED_TAG_NAMES: frozenset[str] = frozenset(
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{
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# System-reserved tags (used by the agent framework for structured context)
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"system-reminder",
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"memory",
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"current_date",
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"think",
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"analysis",
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"subagent_system",
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"skill_system",
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"uploaded_files",
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"todo_list_system",
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# Common prompt-injection tag patterns
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"system",
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"instruction",
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"role",
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"important",
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"override",
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"ignore",
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"prompt",
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}
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)
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# Matches a full blocked tag: <tag>, </tag>, <tag attrs>, <tag/>, bare <tag
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_BLOCKED_TAG_PATTERN = re.compile(
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r"<\s*/?\s*(?:" + "|".join(re.escape(t) for t in sorted(_BLOCKED_TAG_NAMES)) + r")\b[^>]*>?",
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re.IGNORECASE,
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)
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# Plain-text boundary markers (OWASP structured-prompt guidance).
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_USER_INPUT_BEGIN = "--- BEGIN USER INPUT ---"
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_USER_INPUT_END = "--- END USER INPUT ---"
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# Neutralized forms injected when the user's text already contains a marker.
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# These look visually similar but do not match the real boundary delimiters.
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_NEUTRALIZED_BEGIN = "[BEGIN USER INPUT]"
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_NEUTRALIZED_END = "[END USER INPUT]"
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# Matches either boundary token as a standalone line or embedded in text.
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_BOUNDARY_TOKEN_RE = re.compile(
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re.escape(_USER_INPUT_BEGIN) + r"|" + re.escape(_USER_INPUT_END),
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)
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def _escape_tag_match(match: re.Match) -> str:
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"""Escape < and > in a blocked-tag match so it renders as literal text."""
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return match.group(0).replace("<", "<").replace(">", ">")
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def _is_genuine_user_message(message: object) -> bool:
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"""Return True for real user messages, excluding system-injected HumanMessages.
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System-injected context is marked via ``hide_from_ui`` — the same convention
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used by DynamicContextMiddleware and TodoMiddleware.
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"""
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if not isinstance(message, HumanMessage):
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return False
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if message.additional_kwargs.get("hide_from_ui"):
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return False
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if message.name == _SUMMARY_MESSAGE_NAME:
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return False
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return True
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def _check_user_content(text: str) -> str:
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"""Sanitize user content: escape blocked tags, then wrap in boundary markers.
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* Empty/whitespace-only → return unchanged (no marker noise).
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* Blocked tags → HTML-escape ``<``/``>`` (e.g. ``<system>`` → ``<system>``).
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* Boundary tokens in user text → neutralized so they cannot forge boundaries.
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* Already wrapped (strict prefix+suffix) → return text unchanged (idempotent).
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* Otherwise → wrap in boundary markers.
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"""
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if not text.strip():
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return text
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text = _BLOCKED_TAG_PATTERN.sub(_escape_tag_match, text)
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# Idempotency: only skip if text is *exactly* wrapped (prefix+suffix),
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# not if the user merely typed the begin token somewhere.
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if text.startswith(_USER_INPUT_BEGIN) and text.endswith(_USER_INPUT_END):
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# Still neutralize boundary tokens in the inner content — a user
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# can forge the outer wrapping to bypass the neutralization below
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# and inject inner boundary markers (break-out attack).
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inner = text[len(_USER_INPUT_BEGIN) : -len(_USER_INPUT_END)]
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neutralized_inner = _BOUNDARY_TOKEN_RE.sub(
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lambda m: _NEUTRALIZED_BEGIN if m.group(0) == _USER_INPUT_BEGIN else _NEUTRALIZED_END,
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inner,
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)
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if neutralized_inner == inner:
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return text
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return f"{_USER_INPUT_BEGIN}{neutralized_inner}{_USER_INPUT_END}"
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# Neutralize any boundary tokens the user may have embedded, preventing
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# both self-suppression (begin token skips wrapping) and break-out
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# (end token creates a premature boundary inside the payload).
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text = _BOUNDARY_TOKEN_RE.sub(
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lambda m: _NEUTRALIZED_BEGIN if m.group(0) == _USER_INPUT_BEGIN else _NEUTRALIZED_END,
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text,
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)
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return f"{_USER_INPUT_BEGIN}\n{text}\n{_USER_INPUT_END}"
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class InputSanitizationMiddleware(AgentMiddleware[AgentState]):
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"""Guardrail middleware that escapes prompt-injection tags in user input.
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Blocked tags are HTML-escaped (not rejected) so the user's intent is
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preserved while the tags lose their semantic significance. Clean input
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is wrapped in plain-text boundary markers. Transformation is temporary
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(wrap_model_call) — never written to state.
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"""
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@staticmethod
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def _extract_text_from_content(content: str | list) -> tuple[str, list | None]:
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"""Extract concatenated text from a plain-string or content-block-list.
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Returns ``(text, extracted_blocks)``. *extracted_blocks* is None when
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*content* is a string, or the list of text-content-block dicts when a list.
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"""
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if isinstance(content, str):
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return content, None
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if not isinstance(content, list):
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return "", None
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text_parts: list[str] = []
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text_blocks: list[dict] = []
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for block in content:
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if isinstance(block, dict) and block.get("type") == "text" and isinstance(block.get("text"), str):
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text_parts.append(block["text"])
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text_blocks.append(block)
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return "\n".join(text_parts), text_blocks
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@staticmethod
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def _rebuild_content(
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original_content: list,
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processed_text: str,
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text_blocks: list[dict],
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) -> list:
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"""Replace text blocks with a single merged text block, preserving interleaved non-text blocks.
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For ``[text, image, text]`` the image block between the two text blocks
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is kept in place — only the text blocks are collapsed into one.
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"""
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text_block_ids = {id(b) for b in text_blocks}
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first = last = None
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for i, block in enumerate(original_content):
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if id(block) in text_block_ids:
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if first is None:
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first = i
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last = i
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if first is None:
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return original_content
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result: list = [*original_content[:first], {"type": "text", "text": processed_text}]
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# Re-insert any non-text blocks that sat between text blocks
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for i in range(first + 1, last + 1):
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if id(original_content[i]) not in text_block_ids:
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result.append(original_content[i])
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result.extend(original_content[last + 1 :])
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return result
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def _process_request(self, request: ModelRequest) -> ModelRequest:
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"""Return a request with the last genuine user message sanitized.
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Blocked tags are HTML-escaped (not rejected) so the user's intent is
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preserved while the tags lose their semantic significance. Transformation
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is temporary — the original request is never mutated.
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"""
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messages = list(request.messages)
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for i in range(len(messages) - 1, -1, -1):
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msg = messages[i]
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if not _is_genuine_user_message(msg):
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if isinstance(msg, HumanMessage):
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logger.debug(
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"_process_request: skipping non-genuine HumanMessage at pos=%d name=%s hide_from_ui=%s content_preview=%.80r",
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i,
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msg.name,
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msg.additional_kwargs.get("hide_from_ui"),
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msg.content,
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)
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continue
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content = msg.content
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logger.debug("_process_request: found genuine user message at pos=%d content=%.120r", i, content)
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text_content, text_blocks = self._extract_text_from_content(content)
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# No text at all (e.g. image-only message) — pass through
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if not text_content and not isinstance(content, str):
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logger.debug("_process_request: no text content in message — passing through")
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return request
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processed = _check_user_content(text_content)
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if processed == text_content:
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# Already wrapped — no override needed
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return request
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if text_blocks:
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new_content = self._rebuild_content(content, processed, text_blocks)
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else:
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new_content = processed
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# Preserve the pre-sanitization user text so downstream consumers that
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# must see the genuine input (slash skill activation, regenerate) can
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# recover it after the BEGIN/END wrapping. setdefault keeps an existing
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# value (e.g. set by UploadsMiddleware or an IM channel) authoritative.
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preserved_kwargs = dict(msg.additional_kwargs or {})
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preserved_kwargs.setdefault(ORIGINAL_USER_CONTENT_KEY, message_content_to_text(content))
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messages[i] = HumanMessage(
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content=new_content,
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id=msg.id,
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name=msg.name,
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additional_kwargs=preserved_kwargs,
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)
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logger.debug(
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"InputSanitizationMiddleware: original=%r -> processed=%r",
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content if isinstance(content, str) else "[content-blocks]",
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processed,
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)
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return request.override(messages=messages)
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return request
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def _try_process(self, request: ModelRequest) -> ModelRequest:
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"""Sanitize request; fail-open on unexpected errors.
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GraphBubbleUp propagates; other exceptions return the original request.
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"""
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try:
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return self._process_request(request)
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except GraphBubbleUp:
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raise
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except Exception:
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logger.warning(
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"Input guardrail processing failed; passing original request to model",
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exc_info=True,
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)
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return request
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@override
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def wrap_model_call(
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self,
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request: ModelRequest,
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handler: Callable[[ModelRequest], ModelResponse],
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) -> ModelCallResult:
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return handler(self._try_process(request))
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@override
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async def awrap_model_call(
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self,
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request: ModelRequest,
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handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
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) -> ModelCallResult:
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return await handler(self._try_process(request))
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