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.
npx skills add 26zl/cybersec-toolkit --skill ai-threat-testinggit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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/26zl/cybersec-toolkit/ai-threat-testing)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/ai-threat-testing"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/ai-threat-testing/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/26zl/cybersec-toolkit/ai-threat-testing"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/ai-threat-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.00860 |
| Opus 5 | $0.00028 | $0.00430 |
| Sonnet 5 | $0.00011 | $0.00172 |
| Haiku 4.5 | $0.00006 | $0.00086 |
Grade A, and why
ai-threat-testing 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 10d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Threat Testing
Test LLM applications for OWASP LLM Top 10 vulnerabilities using 10 specialized agents. Use for authorized AI security assessments.
Quick Start
1. Specify target (LLM app URL, API endpoint, or local model)
2. Select scope: Full OWASP Top 10 | Specific vulnerability | Supply chain
3. Agents deploy, test, capture evidence
4. Professional report with PoCs generated
Primary Agents
Each agent targets one OWASP LLM vulnerability:
- Prompt Injection (LLM01): Direct/indirect injection, system prompt extraction
- Output Handling (LLM02): Code/XSS injection, unsafe deserialization
- Training Poisoning (LLM03): Membership inference, backdoors, data extraction
- Resource Exhaustion (LLM04): Token flooding, DoS, cost impact
- Supply Chain (LLM05): Dependency scanning, plugin security
- Excessive Agency (LLM06): Privilege escalation, unauthorized actions
- Model Extraction (LLM07): Query-based theft, data reconstruction
- Vector Poisoning (LLM08): RAG injection, retrieval manipulation
- Overreliance (LLM09): Hallucination testing, confidence manipulation
- Logging Bypass (LLM10): Monitoring evasion, forensic gaps
See reference/llm0X-*.md for attack playbooks.
Workflows
Full Assessment (4-8 hours):
- [ ] Reconnaissance
- [ ] Deploy all 10 agents
- [ ] Execute exploits
- [ ] Capture evidence
- [ ] Generate report
Focused Testing (1-3 hours):
- [ ] Select vulnerability (LLM01-10)
- [ ] Deploy agent
- [ ] Execute techniques
- [ ] Document findings
Supply Chain Audit (2-4 hours):
- [ ] Inventory dependencies
- [ ] Scan CVEs
- [ ] Test plugins/APIs
- [ ] Verify model provenance
Integration
Enhances /pentest with AI-specific testing:
- Traditional pentesting + AI threat testing = complete security assessment
- Chain vulnerabilities across traditional and AI vectors
- Unified reporting with CVSS scores
Key Techniques
Prompt Injection: Instruction override, system prompt extraction, filter evasion
Model Extraction: Query sampling, token analysis, membership inference
Data Poisoning: Behavioral anomalies, backdoor triggers, bias analysis
DoS: Token flooding, recursive expansion, context exhaustion
Supply Chain: CVE scanning, plugin audit, model verification
MCP Tool Abuse: MCP server inspectors/debuggers often expose /api/mcp/connect or similar endpoints that accept serverConfig with arbitrary command parameters — unauthenticated RCE. Check for MCP Inspector, MCP Playground, or any MCP debugging UI on non-standard ports (6274, 3000, etc.).
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.
- reference/llm01-prompt-injection.md 2.8 KB
- reference/llm02-insecure-output.md 2.7 KB
- reference/llm03-training-poisoning.md 2.9 KB
- reference/llm04-resource-exhaustion.md 2.8 KB
- reference/llm05-supply-chain.md 3.6 KB
- reference/llm06-excessive-agency.md 3.4 KB
- reference/llm07-model-extraction.md 3.5 KB
- reference/llm08-vector-poisoning.md 3.8 KB
- reference/llm09-overreliance.md 4.0 KB
- reference/llm10-logging-bypass.md 4.3 KB
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.
- 10d ago First seen · 102 lines · 55 tokens per session scan A 645eb1b43a66
ai-threat-testing is a skill published in the GitHub repository 26zl/cybersec-toolkit (52 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 860 once invoked, about $0.0003 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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