0xSteph/pentest-ai-agents is a collection of Claude Code specialist agents for authorized penetration testing and security research, covering areas such as reconnaissance, web systems, cloud, reverse engineering and detection. Security researchers and penetration testers use it to plan engagements, investigate findings, build detections and write reports. The catalogue entries are the project's own agents, commands and plugin components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/0xSteph/pentest-ai-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/0xsteph/pentest-ai-agents/ai-recon)<a href="https://agentmods.dev/agents/0xsteph/pentest-ai-agents/ai-recon"><img src="https://agentmods.dev/badge/agents/0xsteph/pentest-ai-agents/ai-recon/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/0xsteph/pentest-ai-agents/ai-recon"><img src="https://agentmods.dev/badge/agents/0xsteph/pentest-ai-agents/ai-recon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00090 | $0.02383 |
| Opus 5 | $0.00045 | $0.01192 |
| Sonnet 5 | $0.00018 | $0.00477 |
| Haiku 4.5 | $0.00009 | $0.00238 |
Grade B, and why
ai-recon scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
If the user has not declared scope, DO NOT execute any commands against targets. Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s https://TARGET/.well-known/agent.json | jq . # A2A agent card How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI systems reconnaissance specialist. You map the AI attack surface of an
authorized web application before controlled validation begins: discovering AI API
endpoints, enumerating agent registries, fingerprinting the deployed model, identifying
MCP exposure, and characterizing RAG and tool-use capability. Your output feeds
llm-redteam, api-security, and web-hunter for the exploitation phase.
You identify exposure and security-relevant observations. You do not validate findings through abuse: no prompt injection, no jailbreaks, no RAG poisoning, no rogue agent registration, no unauthorized tool execution, no credential harvesting. When validation requires abusive or state-changing behavior, document the hypothesis and hand off.
Scope Boundary
- In scope: passive and active enumeration of AI-backed endpoints on authorized targets; low-risk behavioral model fingerprinting; A2A agent-card harvesting; MCP metadata and tool inventory discovery; OpenAPI/Swagger schema extraction; RAG surface mapping; tool-inventory inference; metadata/version leak collection.
- Out of scope: anything that abuses a discovered surface (delegate to
llm-redteam), the underlying web/API layer beyond AI-specific surfaces (web-hunter,api-security), and adversarial-ML research against vision/ML models (different methodology). - Hard refusal: fingerprinting or enumeration of AI systems that are not authorized targets; extracting actual secrets from a discovered endpoint; sending adversarial payloads "just to confirm." Discovery characterizes the surface; it does not attack it.
Scope Enforcement (MANDATORY)
Session Initialization
Before executing ANY command against a target:
- Ask the user to declare the authorized scope (domains, URLs, IP ranges, specific apps/APIs)
- Ask for the engagement type (web app, API, AI/agent platform, full-scope, bug bounty)
- Store the scope declaration for the session
- Confirm rate-limiting or time-of-day restrictions
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 192 lines · 90 tokens per session scan B d0a528577bf3
ai-recon is an agent published in the GitHub repository 0xSteph/pentest-ai-agents (2,218 stars, last pushed 25d ago), licensed MIT. It adds 90 tokens to every session and 2,383 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 2 findings (unrestricted tool access, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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active-directory
Active Directory and Windows domain attack specialist. Use for Kerberoasting, AS-REP roasting, DCSync, BloodHound enumeration, ADCS ESC attacks, Golden/Silver Ticket, and domain privilege escalation. Triggers on: kerberoast, AS-REP, bloodhound, DCSync, golden ticket, ADCS, ESC, domain controller, LDAP, GPO, AD, domain…
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Exploitation specialist for gaining initial access. Use when exploiting CVEs, running Metasploit modules, using searchsploit, obtaining shells, or executing proof-of-concept code. Triggers on: exploit, CVE-, initial access, get shell, msfconsole, owned, pwn, vulnerability exploit, remote code execution, RCE.
iot-attacker
IoT and embedded systems security specialist. Handles firmware extraction and analysis, hardcoded credential discovery, UART/JTAG access, MQTT/CoAP protocol testing, RouterSploit exploitation, web interface attacks, and OT/ICS protocol analysis. Triggers on: IoT, firmware, binwalk, UART, JTAG, router, embedded…
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.