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/Masriyan/Claude-Code-CyberSecurity-Skillnpx agentmods add skills/masriyan/claude-code-cybersecurity-skill/16-ai-llm-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/masriyan/claude-code-cybersecurity-skill/16-ai-llm-security)<a href="https://agentmods.dev/skills/masriyan/claude-code-cybersecurity-skill/16-ai-llm-security"><img src="https://agentmods.dev/badge/skills/masriyan/claude-code-cybersecurity-skill/16-ai-llm-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/masriyan/claude-code-cybersecurity-skill/16-ai-llm-security"><img src="https://agentmods.dev/badge/skills/masriyan/claude-code-cybersecurity-skill/16-ai-llm-security.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high YARA Match · line 2 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high Prompt Injection · line 72 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
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 | $0.00050 | $0.02342 |
| Opus 5 | $0.00025 | $0.01171 |
| Sonnet 5 | $0.00010 | $0.00468 |
| Haiku 4.5 | $0.00005 | $0.00234 |
Grade B, and why
AI & LLM Security scanned grade B with 1 finding 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 yesterday.
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.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
- Instruction override ("ignore previous instructions and …") Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI & LLM Security
Purpose
Enable Claude to assess the security of AI/LLM-powered applications — chatbots, RAG pipelines, autonomous agents, and tool-using systems. Claude maps findings to the OWASP Top 10 for LLM Applications (2025) and the MITRE ATLAS adversarial-ML knowledge base, builds reproducible attack cases, and recommends concrete mitigations (input/output guardrails, least-privilege tool scopes, content provenance).
Authorization Required: Only test AI systems you own or are explicitly authorized to assess. Prompt-injection and data-exfiltration testing against third-party AI services may violate their terms of service and local law. Confirm written scope before proceeding.
Activation Triggers
This skill activates when the user asks about:
- Prompt injection (direct or indirect), jailbreaks, or system-prompt extraction
- OWASP LLM Top 10, MITRE ATLAS, or AI/ML threat modeling
- Securing a RAG pipeline, vector database, or retrieval layer
- LLM agent / tool-use / function-calling security and confused-deputy risks
- Guardrail, content-filter, or model output validation design
- Sensitive-information disclosure or training-data leakage from a model
- Model / ML supply chain security (model files,
pickle, model registries) - AI red teaming, jailbreak corpora, or automated adversarial prompt generation
- Securing MCP (Model Context Protocol) servers and tool integrations
Prerequisites
pip install requests pyyaml rich
Optional enhanced capabilities:
garak— LLM vulnerability scanner (NVIDIA)promptfoo— prompt/red-team evaluation harness- API key for the target LLM endpoint (test environment only)
modelscan/picklescan— ML model file safety scanning
Core Capabilities
1. Threat Modeling (OWASP LLM Top 10 — 2025)
When asked to threat-model an AI application, map the system against each category and record exposure:
| ID | Risk | What to look for |
|---|---|---|
| LLM01 | Prompt Injection | Untrusted text reaching the prompt (direct & indirect via RAG/web/email) |
| LLM02 | Sensitive Information Disclosure | PII/secrets in prompts, outputs, or training data; system-prompt leakage |
| LLM03 | Supply Chain | Untrusted models, LoRA adapters, datasets, plugins, pickle deserialization |
| LLM04 | Data & Model Poisoning | Tainted training/fine-tune/RAG data; backdoors |
| LLM05 | Improper Output Handling | LLM output passed unsanitized to SQL, shell, browser (XSS), or eval |
| LLM06 | Excessive Agency | Over-broad tool scopes, autonomous side effects, no human-in-the-loop |
| LLM07 | System Prompt Leakage | Secrets/authz logic embedded in the system prompt |
| LLM08 | Vector & Embedding Weaknesses | RAG access-control bypass, embedding inversion, cross-tenant leakage |
| LLM09 | Misinformation | Hallucinations relied on for security/safety decisions |
| LLM10 | Unbounded Consumption | Cost/DoS via token floods, model extraction, wallet-drain |
What ships with it
3 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.
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.
- yesterday Changed ef8fcd4e7156
- 10d ago First seen · 193 lines · 50 tokens per session scan B 842ee532f162
AI & LLM Security is a skill published in the GitHub repository Masriyan/Claude-Code-CyberSecurity-Skill (408 stars, last pushed 2d ago), licensed MIT. It adds 50 tokens to every session and 2,342 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
testing-prompt-injection-in-rag-pipelines
Probe RAG applications for prompt injection via poisoned retrieved context and embedding manipulation.
ai-hacker
Use when aI and LLM security testing — prompt injection, model manipulation, data exfiltration via AI. Use when testing AI-powered applications, finding prompt injection vulnerabilities, or assessing LLM-integrated systems.
llm-prompt-injection
Identify and exploit vulnerabilities in Applications integrating Large Language Models (LLMs). Prompt Injection involves crafting inputs that override the original system instructions provided by the developer, allowing an attacker to exfiltrate data, bypass restrictions, or manipulate the AI's output logic.
markitdown
Convert heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server.
tool-abuse-detection
Detect tool misuse and unexpected code execution via dialogue testing. Use when the agent exposes file, code-execution, or network tools.
generate-rag-dataset
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.