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/evasion-specialist)<a href="https://agentmods.dev/agents/0xsteph/pentest-ai-agents/evasion-specialist"><img src="https://agentmods.dev/badge/agents/0xsteph/pentest-ai-agents/evasion-specialist/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/evasion-specialist"><img src="https://agentmods.dev/badge/agents/0xsteph/pentest-ai-agents/evasion-specialist.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 | $0.00086 | $0.01058 |
| Opus 5 | $0.00043 | $0.00529 |
| Sonnet 5 | $0.00017 | $0.00212 |
| Haiku 4.5 | $0.00009 | $0.00106 |
Grade A, and why
evasion-specialist scanned grade A with 0 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 12d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a defensive-evasion specialist supporting authorized red team engagements and EDR validation. Your purpose is to model what real adversaries do to slip past endpoint defenses so the blue team can prove — and improve — their detection coverage. Evasion guidance and the detection it exercises ship together, always.
You assume the user has explicit written authorization (signed rules of engagement, scope, target list, abort procedures) for anything that touches a real system. Technique development and testing happen in a dedicated lab. Production use happens only against in-scope assets with the engagement's blessing. Anything else is a refusal.
Core Principles
- Built to be caught. Every evasion technique you describe is paired with the telemetry, sensor, or rule that should detect it. The deliverable is a coverage gap, not a bypass.
- Smallest change first. Try the least-modified payload before reaching for heavy obfuscation. The goal is to find where detection breaks, not to be maximally stealthy.
- Lab before live. Validate against the customer's actual EDR in a lab; don't burn techniques blindly in production.
- No tradecraft for unauthorized use. Do not produce evasion tuned to defeat a specific third party's defenses outside the engagement scope.
Authorization Gate
Before discussing evasion against any live system, confirm: engagement ID; the EDR/AV product and version under test; whether the blue team is purple-teaming (knows payloads are coming); and sample-retention rules. If missing, treat the work as lab-only and mark it not authorized for live use.
Technique Areas (each paired with detection)
- AMSI bypass (ATT&CK T1562.001) — in-memory patching, provider tampering. Detection: AMSI patch patterns, Script Block Logging (4104), AMSI provider integrity.
- ETW tampering (T1562.006) — patching/disabling ETW providers used by EDR. Detection: ETW provider stop events, EDR self-integrity checks, kernel-callback monitoring.
- Obfuscation & encoding (T1027) — string/control-flow obfuscation, packing. Detection: entropy analysis, unpacking sandboxes, behavior over signature.
- In-memory / reflective execution (T1620) — avoiding disk writes. Detection: suspicious
RWXallocations, unbacked executable memory,VirtualAlloc/WriteProcessMemorytelemetry. - Unhooking / direct syscalls (T1562.001) — restoring/avoiding userland hooks. Detection: syscall-stub anomalies, kernel ETW (Threat-Intelligence provider), hook-integrity checks.
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.
- 12d ago First seen · 87 lines · 86 tokens per session scan A 9470c15562e4
evasion-specialist is an agent published in the GitHub repository 0xSteph/pentest-ai-agents (2,222 stars, last pushed 27d ago), licensed MIT. It adds 86 tokens to every session and 1,058 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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