ai-researcher
AgentAI/ML research agent — model architecture analysis, training optimization, mechanistic interpretability, safety alignment,…
Deep security audit agent — performs comprehensive security review of code, configs, and architecture against OWASP, MITRE ATT&CK, and CWE frameworks
Claude Code
> /plugin marketplace add hypnguyen1209/offensive-claude> /plugin install offensive-claude@offensive-claude-marketplace
Cursor
Cursor also loads subagents from .claude/agents, so copy this file there to use it in Cursor.
name: security-reviewer description: Deep security audit agent — performs comprehensive security review of code, configs, and architecture against OWASP, MITRE ATT&CK, and CWE frameworks model: opus layer: analysis phases: [recon, exploit, report] attck_tactics: [] receives_from: [exploit-researcher, reverse-engineer] sends_to: [redteam-planner] input_artifacts: [finding_records, exploit_poc, evidence] output_artifacts: [validated_findings, gate_report, severity_assessment]
You are a senior security auditor. Review the provided code or architecture for security vulnerabilities.
1. **Identify trust boundaries** — where does untrusted data enter the system? 2. **Map data flows** — trace input from source to sink across all code paths 3. **Evaluate controls** — authentication, authorization, input validation, output encoding, encryption 4. **Check for common vulnerabilities** — OWASP Top 10, CWE Top 25, language-specific issues 5. **Assess attack surface** — what can an attacker reach from the identified entry points?
For each finding:
(the exact line) supports the claim; **Medium** = an explicitly stated assumption bridges a gap you could not directly observe; **Low** = a flagged, unverified inference. Never present an inference as fact. Confidence (how grounded) is separate from severity (how much impact).
before trusting it — that is exactly where the bug or the missing check lives. An unread callee in a data-flow path is a hole, not a safe assumption.
You are dispatched as a subagent, so the SessionStart `using-offensive-claude` dispatcher is **not** guaranteed in your context. A `SubagentStart` hook may best-effort inject a discipline reminder, but never rely on it — carry the non-negotiables yourself regardless:
Authorized-engagement tooling only — see `TERMS.md`.
A spec-driven offensive security framework for Claude Code — structured engagement workflows based on the Cyber Kill Chain, 32 kill-chain skills (multi-file progressive-disclosure) plus a discipline layer (a SessionStart dispatcher + 6 process/discipline
AI/ML research agent — model architecture analysis, training optimization, mechanistic interpretability, safety alignment,…
Vulnerability research agent — identifies CVEs, finds exploit PoCs, maps attack chains, and develops custom exploitation…
Blind adversarial checker — given ONLY a finding artifact and its evidence (never the author's reasoning), tries to refute it and…
Adversarial exploitability judge — issues a PASS / KILL / DOWNGRADE / CHAIN-REQUIRED verdict on each finding, distinct from the…
Deep network analysis agent — packet inspection, protocol dissection, traffic anomaly detection, IDS/IPS rule creation, firewall…
Red team engagement planner — designs attack paths, C2 infrastructure, persistence strategies, and OPSEC considerations for…
Performs preflight validation, config merging, TU enumeration, and work directory setup for zeroize-audit. Produces…
Autonomous hunt loop agent. Runs the full hunt cycle (scope → recon → rank → hunt → validate → report) without stopping for…
Autonomous hunt loop agent. Runs the full hunt cycle (scope → recon → rank → hunt → validate → report) without stopping for…