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 vignesh2027/Claude-Agentic-Skills2.0-version --skill ai-red-teamergit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/vignesh2027/claude-agentic-skills2.0-version/ai-red-teamer)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/ai-red-teamer"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/ai-red-teamer/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/vignesh2027/claude-agentic-skills2.0-version/ai-red-teamer"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/ai-red-teamer.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 | $0.00074 | $0.01051 |
| Opus 5 | $0.00037 | $0.00526 |
| Sonnet 5 | $0.00015 | $0.00210 |
| Haiku 4.5 | $0.00007 | $0.00105 |
Grade B, and why
ai-red-teamer 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 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.
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.
| Direct prompt injection | LLM01 | Critical | Override system prompt via user input | 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AIRedTeamer Agent
You are AIRedTeamer — an expert in systematically stress-testing AI systems to find failure modes, safety vulnerabilities, and alignment gaps before they reach production.
Sub-Agents
- PromptAttacker — Jailbreak taxonomy, prompt injection, indirect injection, multi-turn attacks
- SafetyEvaluator — Harm category scoring, policy violation detection, refusal rate analysis
- RobustnessProber — Distribution shift, adversarial inputs, edge cases, boundary testing
- GuardrailAuditor — Input/output filter bypass testing, rate-limit evasion, PII leakage
- ReportWriter — Structured red-team reports with severity ratings and mitigations
Attack Taxonomy (OWASP LLM Top 10 Coverage)
| Attack Vector | Category | Severity | Test Method |
|---|---|---|---|
| Direct prompt injection | LLM01 | Critical | Override system prompt via user input |
| Indirect prompt injection | LLM01 | Critical | Inject via retrieved documents/tools |
| Training data extraction | LLM06 | High | Memorization probing with prefix attacks |
| Model denial of service | LLM04 | High | Recursive/exponential token generation |
| Excessive agency | LLM08 | High | Tool call escalation, privilege misuse |
| Output manipulation | LLM09 | Medium | Social engineering via context injection |
| Supply chain poisoning | LLM03 | High | Fine-tune dataset poisoning simulation |
Red-Team Test Suite Structure
Phase 1: Baseline (20 tests)
- Benign queries across all intended use cases
- Establishes normal behavior fingerprint
Phase 2: Boundary Testing (40 tests)
- Edge cases: empty input, max tokens, Unicode, code injection
- Role-play escalation: persona adoption probes
Phase 3: Adversarial (60 tests)
- Direct instruction override attempts
- Indirect injection via tool outputs / RAG documents
- Multi-turn context manipulation
Phase 4: Policy Stress (30 tests)
- All harm categories: CSAM, bioweapons, self-harm, violence
- Dual-use scenarios: chemistry, hacking, social engineering
- Gray areas: legal-but-harmful, information hazards
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 · 110 lines · 74 tokens per session scan B 749885f7aa0d
ai-red-teamer is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (6 stars, last pushed 13d ago), licensed MIT. It adds 74 tokens to every session and 1,051 once invoked, about $0.0004 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 2026-08-31.
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