masriyan/gemini-security-skills · Archived

ai-redteam

AI and LLM red-teaming skill for evaluating prompt injection, jailbreak, data exfiltration, tool abuse, agent hijack, RAG poisoning, model denial of service, and unsafe tool-use chains in Gemini, Claude, GPT, and open-weight models.

First seen Jun 20, 2026

Installation

$ npx skills add masriyan/gemini-security-skills --skill ai-redteam

Summary

  • AI and LLM red-teaming skill for evaluating prompt injection, jailbreak, data exfiltration, tool abuse, agent hijack, RAG poisoning, model denial of service, and unsafe tool-use chains in Gemini, Claude, GPT, and open-weight models.
  • Use to build evaluation harnesses, attack corpora, defensive guardrails, and red-team reports for AI systems you own or are authorized to test.

Stronger alternatives

This repository is archived — consider an actively maintained alternative.

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from masriyan/gemini-security-skills · top by installs.

npx skills add masriyan/gemini-security-skills

Browse all from masriyan/gemini-security-skills

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 3
License LICENSE
Default branch main
Open issues 0
Status Archived

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents claude-code gemini

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,524 B
  • docs SUMMARY.md 394 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

AI Red Team

Authorization Boundary

  • Only target models, agents, and applications the user owns or has written authorization to test.
  • Do not produce working malware, CSAM, weapons synthesis, or content that bypasses model safety for harmful real-world outcomes.
  • Treat findings as defensive: every successful attack must ship with a detection and a mitigation.

Attack Taxonomy

  • Direct injection: instructions in user input override system policy.
  • Indirect injection: hostile content in retrieved docs, web pages, emails, files, image alt-text, or tool output.
  • Jailbreak: roleplay, hypothetical, encoding, obfuscation, multi-turn drift, persona pinning.
  • Tool abuse: forcing browse, shell, code, or MCP tools to act outside scope (SSRF, file read, command exec).
  • Data exfiltration: leaking system prompt, secrets, embeddings, training data, or per-user memory.
  • Agent hijack: rewriting plans, looping, escalating permissions, calling unintended sub-agents.
  • RAG poisoning: index pollution, ranking manipulation, embedding collisions.
  • Resource exhaustion: token bombs, recursive tool calls, prompt-bomb DoS.

Evaluation Workflow

  1. Map the AI surface: model, system prompt, tools, retrievers, memory, output sinks, and downstream callers.
  2. Build a threat model with abuse cases per surface.
  3. Generate a versioned attack corpus (attacks.jsonl) with {id, technique, payload, expected_block, severity}.
  4. Run the harness; record {pass, fail, partial, refusalquality, leakevidence}.
  5. Score with attack success rate per technique, mean time to refuse, and blast radius.

Defensive Output

  • Input/output classifiers and allow-listed tool schemas.
  • Provenance tags on retrieved context; quarantine for untrusted sources.
  • Plan-then-execute pattern with human approval on high-impact tools.
  • Rate limits, recursion caps, and per-tool budgets.
  • Red-team report: top techniques, reproductions, fix owners, regression tests.

Deliverables

  • surface.md, threats.md, attacks.jsonl, harness/, results.csv, report.md, regression-suite/.