Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/hypnguyen1209/offensive-claudenpx agentmods add skills/hypnguyen1209/offensive-claude/ai-securityWrote 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/skills/hypnguyen1209/offensive-claude/ai-security)<a href="https://agentmods.dev/skills/hypnguyen1209/offensive-claude/ai-security"><img src="https://agentmods.dev/badge/skills/hypnguyen1209/offensive-claude/ai-security/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/skills/hypnguyen1209/offensive-claude/ai-security"><img src="https://agentmods.dev/badge/skills/hypnguyen1209/offensive-claude/ai-security.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 67 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium MCP Rug Pull · line 80 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00081 | $0.02888 |
| Opus 5 | $0.00041 | $0.01444 |
| Sonnet 5 | $0.00016 | $0.00578 |
| Haiku 4.5 | $0.00008 | $0.00289 |
Grade A, and why
ai-security scanned grade A 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| Malicious model load | `REDUCE`/`GLOBAL` opcodes invoking `os`/`posix`/`pip`/`runpy`; child proc from python during `torch.load` | fickling/modelscan/picklescan ≥0.0.22 pre-load scan; EDR: python→cmd/curl spawn; prefer Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
| MCP tool poisoning / RCE | Tool description carrying imperative text; `child_process.exec`/shell metachars; tool-def mutation post-install | Pin & hash tool manifests; alert on dynamic re-registration; `execFile` not ` How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI/ML Security
When to Activate
- Red-teaming an LLM/chatbot/copilot for direct & indirect prompt injection and multi-turn jailbreaks.
- Testing a RAG pipeline for document/embedding poisoning, embedding inversion, and cross-tenant retrieval leakage.
- Auditing an AI agent / MCP server for tool poisoning, excessive agency, and command injection (RCE).
- Scanning a model artifact (HuggingFace,
.pt/.pkl/.bin/.gguf) for deserialization payloads before loading it. - Assessing a model API for extraction/distillation, membership inference, and adversarial-suffix robustness.
- Mapping findings to OWASP LLM Top-10 (2025) + MITRE ATLAS for a report.
Technique Map
| Technique | ATT&CK | CWE | Reference | Script |
|---|---|---|---|---|
| Direct prompt injection / system-prompt leak (LLM01/LLM07) | T1059.006, T1606 | CWE-1427 | references/prompt-injection-jailbreak.md | scripts/promptinject_harness.py |
| Multi-turn jailbreak: Crescendo / Skeleton Key | T1059.006 | CWE-1427 | references/prompt-injection-jailbreak.md | scripts/promptinject_harness.py |
| Best-of-N / many-shot / token-smuggling jailbreak | T1059.006, T1027 | CWE-1427 | references/prompt-injection-jailbreak.md | scripts/promptinject_harness.py |
| Indirect injection via ingested content (EchoLeak CVE-2025-32711) | T1190, T1059.006 | CWE-74 | references/prompt-injection-jailbreak.md | scripts/promptinject_harness.py |
| RAG knowledge-base poisoning (PoisonedRAG, 5 docs) | T1195, T1565.001 | CWE-349 | references/rag-vector-poisoning.md | scripts/rag_poisoner.py |
| Embedding-collision / RAG-spraying retrieval hijack | T1195.001 | CWE-349 | references/rag-vector-poisoning.md | scripts/rag_poisoner.py |
| Embedding inversion (reconstruct input from vectors) | T1552, T1213 | CWE-202 | references/rag-vector-poisoning.md | scripts/rag_poisoner.py |
| MCP tool poisoning / rug-pull (CVE-2025-54136/54135) | T1195.001, T1059.006 | CWE-74 | references/agentic-mcp-exploitation.md | scripts/mcp_tool_audit.py |
| MCP command injection RCE (CVE-2025-6514/53107) | T1059, T1059.004 | CWE-78 | references/agentic-mcp-exploitation.md | scripts/mcp_tool_audit.py |
| Excessive agency / confused-deputy tool abuse (LLM06) | T1190, T1648 | CWE-862 | references/agentic-mcp-exploitation.md | scripts/mcp_tool_audit.py |
| Pickle model RCE (CVE-2025-32434, CVE-2024-50050) | T1195.002, T1059.006 | CWE-502 | references/ml-supply-chain.md | scripts/model_scan.py |
| Inference-server pickle RCE (vLLM CVE-2025-32444) | T1190, T1203 | CWE-502 | references/ml-supply-chain.md | scripts/model_scan.py |
| picklescan-bypass smuggling (CVE-2025-1716/1889/1944/1945) | T1195.002, T1027 | CWE-646 | references/ml-supply-chain.md | scripts/model_scan.py |
| Model extraction / distillation (LoRD, logit-layer steal) | T1606, T1213 | CWE-200 | references/model-extraction-adversarial.md | scripts/model_extractor.py |
| Membership inference (training-data exposure) | T1213, T1552 | CWE-200 | references/model-extraction-adversarial.md | scripts/model_extractor.py |
| Adversarial suffix (GCG) / model inversion | T1606 | CWE-1039 | references/model-extraction-adversarial.md | scripts/model_extractor.py |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/agentic-mcp-exploitation.md 7.2 KB
- references/ml-supply-chain.md 6.4 KB
- references/model-extraction-adversarial.md 8.3 KB
- references/prompt-injection-jailbreak.md 9.3 KB
- references/rag-vector-poisoning.md 6.9 KB
- scripts/mcp_tool_audit.py 9.4 KB runs code
- scripts/model_extractor.py 15 KB runs code
- scripts/model_scan.py 10 KB runs code
- scripts/promptinject_harness.py 12 KB runs code
- scripts/rag_poisoner.py 8.1 KB runs code
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 · 111 lines · 81 tokens per session scan A a89224ecb71b
ai-security is a skill published in the GitHub repository hypnguyen1209/offensive-claude (357 stars, last pushed 24d ago), licensed MIT. It adds 81 tokens to every session and 2,888 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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context-retrieval
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