LLM Red-Team

Reflection is not exploitation. A model that echoes your payload is noise; a model that acts on it — leaks its system prompt, emits your canary, calls a tool, renders raw HTML downstream — is a bug. Prove the action, then chain it to concrete impact.

This is the first-class LLM-attack skill. The scanner behind it is bughunter/tools/llm_redteam.py (canary-based corpus runner, /llm-redteam). For auditing an MCP server, use skills/mcp-server-audit; for attacking a deployed agent black-box, use skills/agentic-app-audit.

0. QUICK KILL CHECKLIST

Kill the lead (classify Informational, move on) when:

The model refuses and no canary / leak / tool-call ever lands. "It said something edgy" is not a finding.
System-prompt "leak" is just generic assistant boilerplate ("I am a helpful assistant"), not the target's actual instructions.
Prompt injection fires but reads/changes nothing the current user couldn't already access (no cross-tenant data, no tool with impact).
Output looks like XSS but the host app escapes it before rendering (check the actual downstream sink, not the chat response).
Exfil payload is a markdown image beacon but the client never fetches remote images.

1. ROUTING TABLE — signal on target -> what to run

Signal	Run
Any chat/assistant endpoint	`bughunter/tools/llm_redteam.py --url <endpoint> --field <json-field>` (full corpus)
OpenAI-style API	`--template '{"messages":[{"role":"user","content":"{{PAYLOAD}}"}]}' --response-path choices.0.message.content`
Only want one class	`--category jailbreak` (see `--list-categories`)
Uploads/RAG/"summarize this doc"	indirect-injection + token-smuggling (invisible unicode in the doc)
Agent has tools	excessive-agency category + `skills/agentic-app-audit`
Confirm blind exfil	plant a canary host, correlate with `bughunter/tools/oob_listener.py`

2. ATTACK CLASSES (corpus categories)

The runner fires a canary per run (RT_PWNED_xxxx); a category "lands" when the canary / a real leak / a tool-call shows up in the response.

prompt-injection — direct instruction override, delimiter breaks.
jailbreak — DAN / developer-mode persona escape.
system-prompt-leak — repeat-above, keyword-anchor, scenario-escape.
data-exfil — markdown-image beacon / canary emission to an attacker host.
indirect-injection — payload framed as a retrieved document / tool result (the high-value one for RAG apps).
guardrail-bypass — base64 / split-string instruction smuggling.
multi-turn-crescendo — single-shot simulation of a gradual escalation jailbreak.
cross-lingual — override instruction in another language to dodge English-only filters.
cipher-obfuscation — leetspeak / ROT13-wrapped instruction.
token-smuggling — invisible "Sneaky Bits" unicode (U+2062/U+2064) carrying a hidden instruction (shared encoder with bughunter/tools/hai_payload_builder.py).
excessive-agency — coerce an unsafe tool call / exfil (SSRF via "fetch URL", email send, payment).
insecure-output — get the model to emit raw HTML/script that a downstream sink renders (XSS via AI).

3. CANONICAL OWASP ASI01-ASI10 (single source of truth)

This table is the ONE authoritative ASI mapping for the whole toolkit — skills/bug-bounty and skills/web2-vuln-classes both defer to it. If you edit the taxonomy, edit it here.

ID	Class	What to test	Chain to
ASI01	Prompt Injection / Goal Hijack	Override objectives via direct or indirect injection	IDOR / exfil / tool abuse
ASI02	Tool Misuse	Attacker-controlled tool params ("fetch this URL", code tool)	SSRF / RCE
ASI03	Privilege Compromise	Agent uses broader perms / admin tokens than the user	Priv-esc / cross-tenant
ASI04	Supply Chain	Compromised plugin / MCP server / tool-output poisoning next agent	RCE / data theft
ASI05	Code Execution	Unsafe code-interpreter / sandbox escape	RCE
ASI06	Memory & Context Poisoning	Persistent RAG/memory corruption across sessions/users	Stored injection affecting all users
ASI07	Agent Communication	Inter-agent spoofing / IDOR (agent A reads agent B's context)	Cross-tenant disclosure
ASI08	Excessive Agency	Destructive action without confirmation; cascading failures	Funds/email/delete
ASI09	Insecure Output Handling	AI output rendered as XSS / SQLi / command injection downstream	XSS / injection
ASI10	Sensitive Information Disclosure	Leaks system prompt / keys / configs / user data	Secrets -> deeper access

Triage rule: ASI alone = Informational. It is a bounty only when chained to IDOR / exfil / RCE / ATO with a working PoC.

4. CHAIN TO IMPACT (what makes it payable)

Standalone	Chain	Result
Prompt injection	+ reads another user's conversation/data (IDOR)	High
Indirect injection (RAG doc)	+ persists in memory -> hits every user	High/Critical
Tool misuse "fetch URL"	+ hits internal service / IMDS and returns data	SSRF (Medium/High)
Insecure output	+ the host renders it -> stored XSS -> session theft	High
System-prompt leak	+ the prompt contains an API key / internal URL	High
Excessive agency	+ unconfirmed destructive action (send funds, delete)	High/Critical

5. CONFIRMATION DISCIPLINE (no false positives)

A category only counts when the canary / real leak / tool-call is observed in the response — never on "it sounded compliant."
For blind exfil, require a correlated out-of-band callback (bughunter/tools/oob_listener.py), not just a rendered beacon tag.
For insecure-output XSS, confirm execution at the downstream sink with the XSS verifier (tools/verifiers/xss.py / bughunter/tools/dom_xss_harness.py), not in the chat reply.
Record the exact payload + response excerpt; the report must be reproducible verbatim.

llm-redteam