attenu

Docs › attenu-derive › The engine source: README.md · updated · markdown

attenu-derive

Attenu Derive computes the permission set each AI agent task needs from the app itself — roster, tools, observed calls — for attenu-guard to enforce on every tool call and sub-agent handoff.

attenu.io · Docs · attenu-guard (Attenu Guard, the library that enforces what this engine works out) · Threat model · What is proven

Works with LangGraph · LangChain / deepagents · OpenAI Agents SDK · Google ADK · Pydantic AI · CrewAI · AutoGen · Claude Agent SDK · smolagents · AWS Strands · LlamaIndex · Semantic Kernel · Agno · Haystack · CAMEL-AI · Microsoft Agent Framework · AG2 — unmodified (table below).

attenu-derive reads your agent application — the agents you declared, their roster, the tools each one has, what each task actually calls — and computes the permission set each task needs. attenu-guard enforces that set inside your process, for every agent and every sub-agent handoff, and writes a tamper-evident audit log your auditor verifies offline. You approve the permissions once. Payments and deletes wait for a person you name, as do mail and code execution.

You do not write a policy, and you do not describe one in prose. The input is the app itself.

How it works

  1. Observe. Run your app with the guard in observe mode (free, records only). The recorder captures tool names, scope classes and quantity buckets — never argument values, never prompt text.
  2. Derive. For each agent and task the engine proposes a minimal Authority (scopes + limits + expiry), off the hot path, in layers: L1 structural templates (roster and tool list → scopes) · L2 a versioned tool→scope catalog with domain packs (code, customer service, finance, travel) · L4 fail-closed default — an unknown tool is denied, not guessed. (An L3 constrained-model proposal is designed and deliberately not shipped; see below.)
  3. Meet. The guard grants parent.meet(proposal): a proposal can only ever narrow what the parent holds, so an engine error over-restricts and never widens. An agent hands on no more than it holds.
  4. Approve. attenu onboard prints the day-0 report — what resolves, what is held for a named person, what is unknown — and drafts a domain pack for the gaps. In our own onboarding runs about 30% of tools needed a person to decide, front-loaded on the sensitive ones; the number from your own run is the real one.
  5. Enforce, prove. Observe → shadow (derived permissions evaluated, nothing denied) → enforce. Rollback is one configuration change plus a restart. Every decision lands in the guard's hash-chained audit log; attenu report renders it, attenu verify checks an exported bundle with no access to us.

Permissions come from declared structure — never from prompt text. Prompt injection can talk the model into asking; it cannot widen what the process is allowed to do.

Supported frameworks

The engine feeds attenu-guard, whose adapters attach at each framework's official extension points — the framework stays unmodified. Observe-mode recorders exist for the same set.

Framework Adapter Offline demo + tests Enforced live on a real app
LangGraph / LangChain create_agent / deepagents attenu_guard.adapters.langgraph, .langchain ✓ (travel-booking)
Google ADK .google_adk ✓ (customer-service, financial-advisor, travel-concierge)
CrewAI .crewai ✓ (travel-booking)
OpenAI Agents SDK .openai_agents
Claude Agent SDK .claude_sdk live-verified sub-agent denial
Pydantic AI .pydantic_ai
AutoGen .autogen
smolagents .smolagents
AWS Strands .strands
LlamaIndex .llama_index
Semantic Kernel .semantic_kernel
Agno .agno

Pinned versions run in CI; a weekly job tests the unpinned latest of every framework, so upstream breakage is caught on our side first. MCP and A2A: the guard's wire format carries the chain across services; server-side verification examples are on the roadmap.

Quick start

pip install attenu-derive            # pulls attenu-guard; zero network needed to enforce
attenu init                          # product identity + a local Ed25519 anchor key (no cloud)
attenu demo --scenario fanout        # 9 agents, 18 tools, every disposition, a real anchored ledger — USD 0
attenu coverage  <observed-log>      # what the catalog resolves for these tool calls
attenu onboard   <observed-log>      # day-0 report + a draft domain pack for the gaps
attenu report                        # printable evidence report (HTML) for this product's chains
attenu verify    <bundle.json>       # offline verification of an exported evidence bundle

attenu policy / config / grant / ceiling manage the product's signed configuration revisions: operator grants, declared tools, the ceiling of scopes this product may ever be granted. attenu link / sync / ui connect to the optional Attenu console; the console is never in the deny path and nothing here needs it.

What is measured — and what is not

  • 0 benign blocks across 21 evaluation scenarios on our own sample apps, after a one-time setup pass (docs/GATE-EVIDENCE.md).
  • Every over-reach attempt denied in the adversarial suite; injection families × positions → 0 permissions widened, 0 escalations. The scenarios are self-written; we say so. Both suites are CI gates (eval/).
  • Live, on real applications: a customer-service agent denied mid-run on two different models with the same result, and a financial-advisor analyst held to {web.fetch} ⊂ its coordinator and denied web.search mid-chain (docs/LIVE-ENFORCE.md, docs/A3-FRAMEWORKS.md — Google ADK, CrewAI, LangGraph).
  • Day-0 on a held-out app (travel-concierge, 21 agents): payments withheld, unknowns denied, minutes to full coverage (docs/ONBOARDING.md).
  • p50 0.038 ms per check on the template path.
  • Not shipped: L3 model-based derivation, plan-vs-action divergence scoring, fleet management. Not done: external security audit, SOC 2, penetration test — docs/SECURITY-REVIEW.md is a structured self-review, single reviewer, and says so.

What leaves your environment

In local mode, nothing leaves your environment: no telemetry, no outbound calls; the only egress is a redacted bundle your operator exports by hand. The local audit log holds names, scope classes, quantity buckets and salted hashes — an export fails rather than ship a field it does not recognise. Custody is yours: the anchor key is product-local by default; KMS/HSM custody is validated against a real key (docs/OPS-RUNBOOK.md).

What is different

Three things, in this order. The input is the application — declared agents, roster, tools, observed calls — not a person writing or describing a policy. The result lives inside the guard's meet, so a derived permission set can only narrow what the parent holds and an engine error can only over-restrict — across every sub-agent handoff. And the evidence verifies with the vendor absent: the auditor checks integrity, child ⊆ parent and containment from the exported bundle alone.

Layout

  • src/attenu_derive/vocab/ versioned closed vocabulary (scopes, ceilings, ttl buckets)
  • src/attenu_derive/catalog/ tool→scope catalog + domain packs + coverage tooling
  • src/attenu_derive/derive/ L1–L4 engine: Deriver.propose(event) -> (Authority, DerivationRecord)
  • src/attenu_derive/sample/ observe-mode recorders over the attenu-guard adapters (redact at capture)
  • src/attenu_derive/eval/ adversarial · injection · enforce · corpus-lint gates
  • src/attenu_derive/corpus/ corpus schema, normalizers, gold labels, rubric
  • src/attenu_derive/report.py, evidence_out.py evidence reports and bundles
  • docs/ threat model, live-enforcement record, onboarding, operations, evidence

Non-negotiables

No network call on the deny path · the recorder never stores argument values · corpus blobs never in git · secrets never in the product · every derivation record carries the deciding layer.

License

Apache-2.0 (see LICENSE). Contributions under the DCO. Security policy: the private advisory form on GitHub.