Lyrie.ai — The world's first autonomous AI cybersecurity agent. Built by OTT Cybersecurity LLC.
# Add to your Claude Code skills
git clone https://github.com/OTT-Cybersecurity-LLC/lyrie-aiThe agent that defends what it builds.
No Docker. No yak-shaving. Just pip install lyrie-agent or one curl pipe and you're scanning.
Lyrie is not just another AI assistant. It runs your operations and protects them in the same loop — every layer carries the Lyrie Shield, every patch passes the Shield Doctrine, every finding earns its severity through Lyrie Stages A–F.
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Every AI agent platform treats security as an afterthought. Lyrie treats it as the foundation — and ships the receipts. Every advisory we publish on research.lyrie.ai is backed by a reproducible exploit lab and detection rules in this repo.
Cybersecurity isn't a plugin — it's Layer 1.
v0.8.0)🌊 DeepSeek V4 Pro + Flash — 1.6T-parameter models, 1M context, Thinking/Non-Thinking modes. DEEPSEEK_API_KEY to enable.
📡 Live Threat Feed — lyrie threat-feed pulls verified advisories from research.lyrie.ai in real time. CVE-aware, CVSS-filtered, Shield-attributed.
🔍 SARIF Viewer — framework-free DOM renderer for SARIF 2.1.0 results. Severity badges, file:line refs, groupByRule. Included in @lyrie/ui.
🏛️ New Home: OTT-Cybersecurity-LLC — repo transferred to the official OTT Cybersecurity LLC GitHub org. Old URL auto-redirects.
🔗 LinkedIn Channel — official Lyrie.ai LinkedIn presence live at linkedin.com/company/lyrie-ai
🛡️ The Shield Doctrine — every layer of Lyrie that touches untrusted text passes a Shield gate. (docs/shield-doctrine.md)
🔍 Lyrie Attack-Surface Mapper (/understand) — maps entry points, trust boundaries, tainted data flows, and ranked risk hotspots before any scanner runs.
🧪 Lyrie Stages A–F Validator — every finding earns its severity through six validation gates. Auto-PoCs for confirmed vulns. Auto-remediation summaries. Kills false positives at the source.
🌐 Lyrie Multi-Language Vulnerability Scanners — 8 purpose-built scanners (JS / TS / Python / Go / PHP / Ruby / C / C++) with 53 Lyrie-original detection rules covering OWASP Top 10 + CWE classics.
📡 Lyrie Threat-Intel feed — every PR finding auto-attributed against research.lyrie.ai, CISA-KEV-aligned, with Lyrie Verdict surfaced inline. Bumps severity to critical when KEV-listed.
🔍 Lyrie HTTP Proxy — capture, classify, replay, and fuzz HTTP exchanges. 9 security-signal detectors (missing security headers, weak cookie flags, open CORS, secrets in responses, GraphQL introspection, auth tokens in URLs, verbose 5xx errors, and more). 7 structured mutators for replay-based testing.
🆓 Lyrie OSS-Scan service — free public scan at research.lyrie.ai/scan. Submit any GitHub / GitLab / Bitbucket / Codeberg repo URL, get a Lyrie report (Mapper + Scanners + Stages A–F + auto-PoC) in seconds.
🚀 Lyrie Pentest GitHub Action — Shield-scans every PR, posts a single-comment-per-PR Markdown summary, uploads SARIF to Code Scanning, blocks merges on fail-on threshold.
🧠 FTS5 cross-session memory — bm25-ranked recall + LLM-summarized session digests, every snippet Shield-gated.
✏️ Diff-view edits with approval gates — apply_diff produces unified diffs, never overwrites whole files; Shield scans every patch before it touches disk.
🔌 MCP adapter (@lyrie/mcp) — Lyrie speaks fluent Model Context Protocol both as client and server.
🚪 DM pairing — unknown senders can't reach the agent without operator approval. Three modes: open / pairing / closed.
🩺 lyrie doctor — read-only environment, channel, and security self-diagnostic with --json for CI.
🧬 LyrieEvolve — the agent scores every task, auto-generates reusable skills from wins, retrieves top-3 past successes as context before each new task, and runs nightly GRPO fine-tuning on your own GPU. Domain-specific rewards for cyber, SEO, trading, and code. (docs/evolve.md)
☁️ Pluggable execution backends — run Lyrie scans locally, in a Daytona devbox, or as a Modal serverless function. Same Shield Doctrine, same SARIF, different host.
📡 9 multi-channel adapters — Telegram, WhatsApp, Discord, Slack, Matrix, Mattermost, IRC, Feishu, Rocket.Chat, WebChat — one inbox, all secured.
🔴 LyrieAAV — Autonomous Adversarial Validation: 50+ attack vectors across all OWASP LLM Top 10 categories, automated verdict scoring, SARIF output, Python + TypeScript SDKs. Beats Audn.AI at its own game. (docs/aav.md)
LyrieAAV is Lyrie's AI red-teaming engine. It attacks deployed AI agents and LLMs to find security vulnerabilities before adversaries do.
# Attack any OpenAI-compatible endpoint
bun run scripts/redteam.ts http://localhost:11434/v1 --model llama3 --dry-run
bun run scripts/redteam.ts https://api.openai.com/v1 --api-key $KEY --fail-on high
bun run scripts/redteam.ts http://myapp.com/v1 --output sarif --out scan.sarif
| Feature | LyrieAAV | Audn.AI |
|---|---|---|
| Attack vectors | 50+ | ~20 |
| OWASP LLM Top 10 | All 10 | Partial |
| Auto verdict scoring | ✅ Regex-based | Manual review |
| NIST AI RMF refs | ✅ Every vector | ❌ |
| EU AI Act refs | ✅ Every vector | ❌ |
| TypeScript SDK | ✅ | ❌ |
| Streaming API | ✅ scanStream() | ❌ |
| Retry variants | ✅ 3 per vector | ❌ |
| DeepSeek V4 Pro support | ✅ 1.6T params | ❌ |
| Open source | ✅ MIT | Proprietary |
| Price | Free | Paid |
Usage: lyrie redteam <endpoint> [options]
--api-key <key> API key for the target endpoint
--model <model> Model name (default: gpt-3.5-turbo)
--preset <name> Attack preset: entra|state-actor|critical|all
--categories <cats> OWASP categories (e.g. LLM01,LLM06)
--severity <level> Min severity: critical|high|medium|low
--mode <mode> blackbox|greybox|whitebox
--system-prompt <sp> Inject system prompt
--concurrency <n> Parallel probes (default: 3)
--output <fmt> markdown|sarif|json
--out <path> Write to file
--fail-on <sev> Exit 1 on findings >= severity
--dry-run Simulate without HTTP requests
Preset examples:
lyrie redteam <endpoint> --preset entra --dry-run # Entra priv-esc (4 vectors)
lyrie redteam <endpoint> --preset state-actor --dry-run # Nation-state attacks (6 vectors)
Full architecture: docs/aav.md
Assess your AI deployment against NIST AI RMF and EU AI Act requirements.
# Interactive NIST AI RMF assessment (8 governance questions)
lyrie governance assess --interactive
# Auto-infer from agent config file
lyrie governance assess --config ./agent-config.json --out report.json
# Analyze an agent's tool permissions for risk
lyrie governance permissions ./tools-manifest.json
# Get JSON output
lyrie governance permissions ./agent.config.json --json --out perms.json
Scores your AI deployment 0–100 across 4 NIST AI RMF functions:
| Function | Covers | |----------|--------| | GOVERN | AI inventory, permission scoping | | MAP | Vendor assessment, data governance | | MEASURE | Audit logging, model drift monitoring | | MANAGE | Human oversight, incident response |
Maturity levels: None → Initial → Developing → Defined → Managed → Optimizing
EU AI Act classification: High-Risk / Limited-Risk / Minimal-Risk
Scans your agent's tool manifest and flags permission risks:
| Risk Level | Example Tools | Issue |
|-----------|---------------|-------|
| 🔴 CRITICAL | execute_code, assign_role, process_payment | Must have human approval + audit log |
| 🟠 HIGH | write_file, user_data | Needs scoping