canari-forensics 0.1.0

Scan historical LLM traces for credential leaks and prompt-injection evidence

canari-forensics

Scan your LLM logs for breaches that already happened.

LLM applications can leak internal context through prompt injection attacks. Your firewall never flags it because the exfiltration looks exactly like a legitimate API response. Most teams find out weeks later - if ever.

Canari Forensics scans your existing LLM conversation logs and tells you definitively whether you have had any successful prompt injection or credential leakage before you were monitoring. Exact pattern matching, no classifiers, no false positives. Runs locally in under a minute. No data leaves your environment.

Install

pip install canari-forensics

If your environment blocks package installs, you can run directly with python3 -m canari_forensics .... After install, run canari ... directly.

Quick start

# 1) Scan OTEL JSON exports (generic/datadog/honeycomb via --provider)
canari forensics scan \
  --source otel \
  --provider generic \
  --logs ./otel-traces \
  --file-pattern '*.json' \
  --out ./forensics-scan.json

# 2) Generate enterprise audit outputs
canari forensics report \
  --scan-report ./forensics-scan.json \
  --client "Acme Corp" \
  --application "AI Gateway" \
  --out-pdf ./audit-report.pdf \
  --out-evidence ./canari-evidence.json \
  --bp-dir ./tests/attacks

Staged audit workflow

# initialize audit workspace
canari forensics audit init \
  --name "Q1 2026 AI Gateway Audit" \
  --source otel \
  --provider generic \
  --logs ./otel-traces \
  --client "Acme Corp" \
  --application "AI Gateway"

# run scan and report using stored metadata
canari forensics audit scan --audit-id q1-2026-ai-gateway-audit
canari forensics audit report --audit-id q1-2026-ai-gateway-audit

One-command audit from config

cp .canari.yml.example .canari.yml
canari forensics audit run --config .canari.yml

Custom pattern packs

canari forensics report \
  --scan-report ./forensics-scan.json \
  --client "Acme Corp" \
  --application "AI Gateway" \
  --out-pdf ./audit-report.pdf \
  --out-evidence ./canari-evidence.json \
  --bp-dir ./tests/attacks \
  --patterns-file ./custom_patterns.json

The JSON file should contain either {"patterns": [...]} or a top-level array, where each pattern has: pattern_id, name, severity, confidence, kind, regex.

Local demo checkpoint

./scripts/demo_local_audit.sh

Real-time OTLP receiver

canari forensics receive \
  --host 0.0.0.0 \
  --port 4318 \
  --db ./canari-forensics.db

Outputs:

• Scan JSON with normalized conversation turns
• Evidence JSON with findings and metadata
• PDF audit report for executive review
• .bp.json snapshots for BreakPoint CI workflows