vaara 0.40.3

Adaptive AI Agent Execution Layer for risk scoring, audit trails, and regulatory compliance

Vaara is the runtime evidence layer for AI Act compliance. Open source, no SaaS, no telemetry.

Vaara intercepts agent tool calls, scores each one with a conformal risk interval, and writes a hash-chained audit record. Online learning across five expert signals via Multiplicative Weight Update. Distribution-free conformal coverage on the score. An external auditor can verify these properties without trusting your stack. Orchestration toolkits and identity layers (Microsoft Agent Governance Toolkit, others) sit on top.

Numbers

Held-out TEST recall 84.7% (95% Wilson [82.4, 86.7]) at FPR 4.1% [2.9, 5.7]. Phase 1 PAIR scale-up to n=300 per attacker family lands at 88.1% [85.8, 90.1]. Under BIPIA-pressure context, false-positive rate on benign tool calls 1.2% [0.4, 3.6] across four agent backends (Claude Haiku 4.5, Llama-3.1-8B, Mistral-7B, Qwen-2.5-7B). Multi-attacker PAIR ASR 0/25 across three different attacker models with identical seeds. 140 µs mean / 210 µs p99 inference latency on commodity CPU (excluding one-time embedding model load). Every number reproducible end-to-end via make bench.

• 12,155-entry adversarial corpus (250 hand-curated + 11,905 LLM-generated), 70/15/15 split stratified by (category, source)
• Classifier v9 with 236 hand-features + 384-dim MiniLM embeddings at calibrated threshold 0.9150 on held-out TEST n=1,827: recall 84.7% [82.4, 86.7] at FPR 4.1% [2.9, 5.7]
• Multi-attacker PAIR robustness: 0/25 successes per attacker across Qwen2.5-32B, Qwen2.5-72B, Llama-3.3-70B hitting identical seed indices, Wilson upper 13.3%
• BIPIA-pressure FPR on benign tool calls 1.2% [0.4, 3.6] across four agent backends, n=244 benign tool calls under context.source=injected_via_bipia_<class>
• Chain of custody: corpus manifest SHA → split manifest SHA → training commit → bundle SHA, all locked and printed by every script
• 140 µs mean / 210 µs p99 inference latency, commodity CPU
• Distribution-free conformal coverage on the score
• MWU regret bound O(sqrt(T log N))
• vaara-bench-v0.39: current methodology, chain of custody, ship-gate record. v9 retrain on BIPIA-augmented corpus with follows upweighted (--follow-weight 8.0), calibrated to T=0.9150 at a 5% FPR target on v035 VAL. BIPIA-pressure FPR collapses from 35.2% on v8 to 1.2% on v9. In-distribution recall flat within Wilson intervals. Found-and-fixed in tree: auto-labeller example.com placeholder false-positive rule (42 → 14 true follows across four backends). Historical bench docs live under bench/ for chain-of-custody continuity.
• vaara-bench-v1: 77-trace synthetic-corpus regression baseline with frozen methodology, 100% soft TPR, 0% hard FPR

Each figure is reproducible from the public corpus or the bench pipeline in bench/.

Install

pip install vaara

Python 3.10+. Zero runtime deps. Optional XGBoost classifier: pip install vaara[ml].

Releases ship with SLSA Build Level 3 provenance. Verify with slsa-verifier verify-artifact.

Quick start

from vaara.pipeline import InterceptionPipeline

pipeline = InterceptionPipeline()
result = pipeline.intercept(
    agent_id="agent-007",
    tool_name="fs.write_file",
    parameters={"path": "/etc/service.yaml", "content": "..."},
    agent_confidence=0.8,
)
if result.allowed:
    pipeline.report_outcome(result.action_id, outcome_severity=0.0)
else:
    print(result.reason)

report_outcome closes the loop. MWU reweights signals based on which ones predicted the outcome.

What evidence looks like

vaara compliance report --format json against a real audit trail produces an article-level evidence record an auditor can read directly. Status is reported honestly: articles without recorded events return evidence_insufficient, not a rubber-stamp.

{
  "system_name": "Acme HR Assistant",
  "overall_status": "evidence_insufficient",
  "trail_integrity": {"size": 105, "chain_intact": true},
  "articles": [
    {"article": "Article 12(1)", "title": "Record-Keeping (Logging)",
     "status": "evidence_sufficient", "strength": "strong", "evidence_count": 105},
    {"article": "Article 9(2)(a)", "title": "Risk Identification and Analysis",
     "status": "evidence_sufficient", "strength": "strong", "evidence_count": 35},
    {"article": "Article 15(1)", "title": "Accuracy, Robustness and Cybersecurity",
     "status": "evidence_insufficient", "strength": "absent", "evidence_count": 0}
  ]
}

The same data renders as a styled PDF for Notified Bodies (vaara compliance report --format pdf, requires pip install 'vaara[pdf]'), a static HTML dashboard (vaara compliance dashboard), or a Sigstore-signed regulator-handoff envelope (vaara trail export, optional ML-DSA-65 / FIPS 204 post-quantum signer via pip install 'vaara[pq]').

Each article verdict carries verdict_inputs (threshold-vs-observed snapshot), verdict_reasons (rationale lines), and contributing_events (the audit records the verdict sits on, with a drill_down of the data that fed the risk/decision/outcome). Reviewers can trace status → threshold delta → concrete event without re-running the engine.

Framework adapters

Native adapters in src/vaara/integrations/ route the major Python agent frameworks through Vaara's pipeline. Each intercepts via the framework's own callback or hook surface, scores, gates, and emits the same audit events as a direct pipeline.intercept(). Frameworks are not hard dependencies (lazy import, duck typing).

Framework	Entry point	Use
LangChain	VaaraCallbackHandler, vaara_wrap_tool	Slots into config={"callbacks": [...]} or wraps per-tool
CrewAI	VaaraCrewGovernance	Wraps a crew so every agent action passes through scoring + audit
OpenAI Agents SDK	VaaraToolGuardrail, vaara_wrap_function	Function-tool wrap, compatible with Responses API and Agents-SDK tracing
MCP server	vaara.integrations.mcp_server	Exposes scoring, audit, policy reload as MCP tools

All four share the same in-process pipeline, so audit records hash-chain together regardless of which framework the action came through. For Vaara in front of an upstream MCP server, see the MCP proxy section below.

Upstream-signal adapters (cloud + OSS guardrails)

Adapters route findings from cloud and OSS guardrails into Vaara's audit trail and OVERT envelope. The filter runs in the deployer's environment as an upstream signal. Vaara records the verdict, normalises 68 provider categories onto a shared vocabulary, and tags each finding against the relevant AI Act articles. Article-by-article mapping in COMPLIANCE.md.

Provider	Adapter	Extra	Wraps
AWS Bedrock Guardrails	BedrockGuardrailsAdapter	vaara[bedrock]	ApplyGuardrail across five Bedrock policy buckets
Azure AI Content Safety	AzureContentSafetyAdapter	vaara[azure-content-safety]	analyze_text, Prompt Shields, Protected Material, Groundedness
GCP Model Armor	GcpModelArmorAdapter	vaara[gcp-model-armor]	sanitize_user_prompt, sanitize_model_response
NVIDIA NeMo Guardrails	NemoGuardrailsAdapter	vaara[nemo-guardrails]	GenerationResponse.log.activated_rails (input / dialog / output / retrieval)
Guardrails AI	GuardrailsAIAdapter	vaara[guardrails-ai]	ValidationOutcome.validation_summaries from Guard.parse / Guard.validate
LLM Guard	LLMGuardAdapter	vaara[llm-guard]	scan_prompt / scan_output, parses (sanitized, results_valid, results_score)
Rebuff	RebuffAdapter	vaara[rebuff]	DetectResponse across heuristic, model, vector layers + canary-word leak check

Each adapter returns a ContentSafetyFinding the deployer routes into pipeline.intercept(context=finding.to_audit_context()). The mapping table lives at src/vaara/integrations/_content_safety_articles.py. Article-level rationale in COMPLIANCE.md and COMPLIANCE.md.

HTTP API

The same scorer and audit trail are available over HTTP for non-Python agents and for control planes that prefer a network boundary. Install with the server extra:

pip install 'vaara[server]'
vaara serve --host 0.0.0.0 --port 8000

curl -sX POST http://localhost:8000/v1/score \
  -H 'content-type: application/json' \
  -d '{"tool_name":"tx.transfer","agent_id":"agent-007","base_risk_score":0.5}'

The wire contract is in docs/openapi.yaml. Integration recipes under examples/recipes/. Operator endpoints include POST /v1/policy/reload for atomic hot policy swap, and POST /v1/detect/injection and POST /v1/detect/pii as named detectors with matching CLI subcommands that exit non-zero on detection for CI gating.

TypeScript client

The first-party TypeScript client lives at clients/ts and ships on npm as @vaara/client. Typed wrappers over every v1 endpoint, Node 18+, ESM, declarations shipped. JS/TS agents (LangChain.js, Vercel AI SDK, MCP, any Node service) can call Vaara without a Python sidecar.

npm install @vaara/client

import { VaaraClient } from "@vaara/client";
const vaara = new VaaraClient({ baseUrl: "http://localhost:8000" });
const r = await vaara.score({ tool_name: "tx.transfer", agent_id: "agent-007", base_risk_score: 0.6 });
if (r.decision === "deny") throw new Error("blocked");

MCP proxy (Vaara as a transparent governance layer)

vaara.integrations.mcp_proxy.VaaraMCPProxy sits between an MCP client (Claude Code, Cursor, any MCP-capable host) and an upstream MCP server. Every tools/call from the client routes through Vaara's interception pipeline before reaching the upstream. Allowed calls forward transparently and report the upstream outcome back to the scorer. Blocked calls return an MCP isError: true response with the block reason. The initialization handshake and notifications/* forward unchanged. tools/list, resources/list, resources/read, prompts/list, and prompts/get route through the operator perimeter before reaching the client or upstream.

vaara-mcp-proxy \
  --upstream npx --upstream-arg -y --upstream-arg @sap/mdk-mcp-server \
  --db ./mcp_audit.db

Point your MCP client at the proxy instead of the upstream. The audit chain captures every tool call without changing client or upstream behavior. Distinct from mcp_server, which exposes Vaara itself as an MCP server for agents that consult Vaara as a tool.

Fleet shape (v0.40): one proxy, many upstreams, multi-tenant policy

vaara-mcp-proxy also runs over Streamable HTTP with fan-out, so one process can serve a fleet of upstream MCP servers:

vaara-mcp-proxy \
  --transport http \
  --http-host 127.0.0.1 \
  --http-port 8765 \
  --upstream 'github=npx -y @github/mcp-server' \
  --upstream 'sap=npx -y @sap/mdk-mcp-server'

Each POST /mcp reads two headers. X-Vaara-Upstream picks the upstream slot. X-Vaara-Tenant scopes the policy, audit chain, and OVERT envelope for that call. Single-upstream deployments keep the v0.39 silent-default contract. Multi-upstream deployments require X-Vaara-Upstream per call and return 400 with the available slot list when the header is missing.

The reference HTTP API server (vaara serve --policy-dir DIR) loads one YAML or JSON policy per file in the directory (filename stem becomes the tenant_id, default.yaml lands in the fallback slot) and hot-reloads per tenant via POST /v1/policy/reload with a tenant_id body field or X-Vaara-Tenant header. The scorer dispatches allow and deny thresholds per call against the calling tenant's policy at evaluate() time.

Operator perimeter: tool, resource, prompt filtering

The proxy accepts repeatable --allow-tool NAME / --deny-tool NAME, --allow-resource URI / --deny-resource URI, and --allow-prompt NAME / --deny-prompt NAME flags. Filtered tools are dropped from tools/list responses before the client sees them and any matching tools/call is rejected at the proxy perimeter without contacting the upstream. The same shape extends to resources/list + resources/read and prompts/list + prompts/get. Denylist wins on overlap with allowlist. No flags = passthrough. Every allowed resources/read and prompts/get writes a request+decision audit pair to the hash chain so a regulator can reconstruct exactly which resources the agent read and which prompts it retrieved. Read-oriented MCP surfaces do not run through the risk scorer. The operator perimeter is the gate, the audit chain is the evidence.

OVERT envelopes per governed interaction turn on with --overt-signing-key, --overt-operator-key, --overt-receipts-dir. Wire format and verifier covered in the OVERT 1.0 attestation section below. Long-running tools' notifications/progress and notifications/message route through the same audit pair and OVERT envelope, correlated to the originating call via _meta.progressToken.

Worked examples:

• examples/github-mcp-proxy-demo/ — Vaara in front of github/github-mcp-server, 42 tools, hash-chained audit trail recorded end-to-end.
• examples/sap-mcp-proxy-demo/ — Vaara in front of community SAP MCP servers (SAP/mdk-mcp-server, mario-andreschak/mcp-abap-abap-adt-api, lemaiwo/btp-sap-odata-to-mcp-server).

OVERT 1.0 attestation

What. OVERT 1.0 is an open standard for runtime trust in AI systems (overt.is, authored by Glacis Technologies, published 25 March 2026). It defines a signed, schema-closed envelope a relying party can verify offline without trusting the emitter.

Why. A regulator, auditor, or customer can confirm that a runtime decision actually happened the way you say it did, without reading your code or trusting your stack.

How Vaara emits it. Vaara is the Arbiter in OVERT terms and ships Protocol Profile 1.0 Base Envelopes (canonical CBOR per RFC 8949, Ed25519 signatures, HMAC-SHA256 keyed commitments, closed 9-field schema, IEEE-754 float rejection) alongside every audit record when attestation is enabled.

pip install 'vaara[attestation]'

from vaara.attestation.overt import emit_base_envelope, make_request_commitment, encoder_binary_identity

envelope = emit_base_envelope(
    signing_key=key,
    request_commitment=make_request_commitment(payload, operator_key=op_key),
    encoder_binary_identity=encoder_binary_identity(arbiter_version=f"vaara/{vaara.__version__}", policy_hash=ph),
    non_content_metadata={"action_class": "tx.transfer", "decision": "escalate"},
    monotonic_counter=42,
    arbiter_instance_identifier=uuid_bytes,
)

vaara overt verify RECEIPT.cbor --pubkey-file PUB.bin validates any canonical-CBOR Base Envelope. The verifier reads only the wire format and takes no dependency on Vaara's emitter, so any conformant implementation can route through it.

Adjacent surfaces: a reference Phase 3 IAP (vaara.attestation.iap) notary-signs the Provisional Receipt and anchors it in a transparency log (sigstore Rekor swappable); an S3P emitter (vaara.attestation.s3p) ships Clopper-Pearson aggregate intervals; an experimental hardware TEE hook (vaara.attestation.tee) binds an envelope to an AMD SEV-SNP attestation report via SHA-512(canonical_cbor(envelope)) in REPORT_DATA.

Architectural framing and the OVERT 1.0 Part 3 control walk in COMPLIANCE.md.

Where things live

Path	Contents
docs/formal_specification.md	MWU regret bound, conformal coverage, security properties
docs/conformal-prediction.md	Plain-language explainer for compliance reviewers and legal counsel
COMPLIANCE.md	EU AI Act (Art. 9, 11 to 15, 61) and DORA (Art. 10, 12, 13) mapping, eval numbers, PAIR calibration
VERDICTS.md	Per-article evidence sufficiency thresholds and decision tree
CHANGELOG.md	Version-by-version feature evolution
PRIOR_ART.md	When each Vaara concept first shipped, and a neutral list of adjacent published work
OWASP_AGENTIC.md	Vaara mapping to OWASP Top 10 for Agentic Applications 2026 (ASI01 to ASI10)
OVERT_CONTROLS.md	Vaara mapping to OVERT 1.0 Part 3 Agentic AI Controls (TOOL-, MCP-, MULTI-, CAP-, DISC-, HITL-, DRIFT-*)
docs/mit_ai_risk_repository_mapping.md	Vaara coverage map against the MIT AI Risk Repository v4 (1,835 risk-bearing entries across 7 domains)
docs/signing-keys.md	Release signing and verification
SECURITY.md	Security policy and reporting
CONTRIBUTING.md	Contribution guidelines
src/vaara/integrations/	LangChain, OpenAI Agents SDK, CrewAI, MCP, Bedrock, Azure, GCP
src/vaara/audit/	Hash-chain trail, SQLite backend, append-only WAL
src/vaara/policy/	YAML / JSON policy schema, vaara policy validate and vaara policy test
src/vaara/sandbox/	Synthetic-trace cold-start calibration

Acknowledgements:

• Vaara is listed in the industry acknowledgements of the IMDA Model AI Governance Framework for Agentic AI v1.5 (Singapore, 20 May 2026).
• The AMD AI Developer Program ran a coordinated multi-channel developer testimonial of Vaara in May 2026.
• Article 14 runtime: why oversight of agentic AI has to be evidenced as action, not model is the position post on the EU Apply AI Alliance Futurium.

Vaara helps deployers assemble evidence for their own conformity work. It does not certify compliance or constitute legal advice. Deployers own their obligations under the EU AI Act and other applicable law.

License

Apache 2.0. See LICENSE.