Agent Sleuth

Prevents untrusted data from triggering consequential actions in your agent.

Agent Sleuth is an in-process information-flow-control (IFC) library for LLM agents. It stops untrusted data (web pages, email bodies, tool outputs, retrieved documents) from driving consequential actions (sending email, writing files, posting to external services).

The mechanism is value-level provenance lineage tracked at the tool-I/O boundary — not taint-tracking through the model's forward pass. When an untrusted tool returns data, we fingerprint the specific values in it. When a later consequential ("sink") call's arguments carry those fingerprinted values — verbatim or via structured-field tracking — that is a deterministic, classifier-free provenance edge. A small policy fires: untrusted-origin value reaching a non-allowlisted external sink → block or confirm.

• Deterministic, not a classifier. The guarantee is a value-lineage match, never an LLM judging intent.
• Zero extra LLM calls on the common path.
• Drop-in. Three lines, zero changes to your agent.
• Audit-mode first. Observe for a week, then switch to enforce.

Install

pip install agent_sleuth                 # core, zero agent-framework deps
pip install 'agent_sleuth[langchain]'    # + LangChain callback handler
pip install 'agent_sleuth[config]'       # + YAML config loading
pip install 'agent_sleuth[dev]'          # + pytest/ruff

Three-line integration (raw / custom agent)

from agent_sleuth import Sleuth

sleuth = Sleuth(
    untrusted=["read_email", "fetch_url", "search_web"],
    consequential=["send_email", "write_file", "post_slack"],
    destinations=["me@myco.com"],   # your own channels = trusted egress
    mode="audit",                   # → "enforce" once you trust it
)
sleuth.reset(query="summarize my emails and send a report to my boss")

# wrap your tools (or pass sleuth.handler to a LangChain agent — see below)
fetch_url  = sleuth.track(fetch_url)
send_email = sleuth.track(send_email)

# ... run your agent ...
print(sleuth.report())

You can also skip the explicit lists entirely — Sleuth() uses name-based defaults (tools containing read/fetch/search/get/... are untrusted; send/write/post/delete/... are consequential).

LangChain (zero changes to your agent)

from agent_sleuth import Sleuth

sleuth = Sleuth(agent=your_langchain_agent, mode="audit")
result = sleuth.run("summarize my emails and send a report to my boss")
print(sleuth.report())

Sleuth.run() resets taint state, stashes the trusted query, and attaches the IFCCallbackHandler to your agent — no edits to your chain.

What a caught attack looks like

BLOCKED: send_email() called with tainted inputs
  Taint source: fetch_url (step 2, untrusted)
  Injected value detected in argument: to="attacker@evil.com"
  Lineage: fetch_url (step 2) → value "attacker@evil.com" → send_email.to
  Destination: attacker@evil.com (not allowlisted)
  Reason: untrusted-origin value reached a consequential sink
  Action: blocked, call halted

Modes

• audit (default): detect + log + render the trace; never block.
• enforce: raise TaintViolationError and halt the offending sink call.
• confirm: surface the violation to a callback for an allow/deny decision before dispatch.

Honest coverage envelope (v0)

Sound on the verbatim/structured-exfil class. Zero extra LLM calls on the common path. Drop-in. Laundering (base64/paraphrase of a secret) and pure control-flow hijack (a sink call whose arguments carry no untrusted bytes) are explicitly out of scope for v0 — documented non-goals, not bugs. Control-flow integrity (the plan-allowlist) and a configurable allow/denylist with deny-over-allow precedence land in v1.

Benchmark

PYTHONPATH=. python benchmarks/agentdojo/run.py

A self-contained reproduction of AgentDojo-style indirect-injection tasks (real AgentDojo needs a live LLM + API keys; see the harness docstring for the thin real-AgentDojo wiring). Reports ASR (attack success rate) and utility per mode.

Develop

pip install -e '.[dev,langchain,config]'
pytest

See AGENT_SLEUTH_ARCHITECTURE.MD for the full design.