canari-forensics 0.1.5

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.

Demo

Run the local demo to see Canari Forensics find incidents in sample logs:

./scripts/demo_local_audit.sh

MLflow / OTEL Integration

If you are using MLflow to trace your LLM applications, export your traces and point Canari Forensics at the output:

# Export traces from MLflow
mlflow traces export \
  --experiment-id YOUR_EXPERIMENT_ID \
  --output-dir ./traces/

# Scan with Canari Forensics
canari forensics scan \
  --source otel \
  --provider generic \
  --logs ./traces/ \
  --out ./forensics-scan.json

Compatible with local MLflow and any platform that exports OTEL traces.

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

Related Tools

• BreakPoint — catch regressions before you ship
• Canari — detect attacks in real time
• Canari Forensics — audit logs for past breaches

Maintainer

Maintained by Christopher Holmes Silva.

• X: https://x.com/cholmess
• LinkedIn: https://linkedin.com/in/cholmess

Feedback is welcome from developers shipping LLM applications.