Agentic Bug Hunter is a terminal toolkit that uses AI to investigate security targets, test for vulnerabilities, validate findings, and write bug bounty reports. It is for ethical hackers submitting findings to platforms such as HackerOne, Bugcrowd, Intigriti, or Immunefi, and can resume previous sessions. The catalogue entries package commands, skills, agents, instructions, hooks, and settings for using the toolkit.
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.
git clone --depth 1 https://github.com/Awarexone/Agentic-Bug-HunterWrote 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/commands/awarexone/agentic-bug-hunter/llm-redteam)<a href="https://agentmods.dev/commands/awarexone/agentic-bug-hunter/llm-redteam"><img src="https://agentmods.dev/badge/commands/awarexone/agentic-bug-hunter/llm-redteam/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/commands/awarexone/agentic-bug-hunter/llm-redteam"><img src="https://agentmods.dev/badge/commands/awarexone/agentic-bug-hunter/llm-redteam.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 | $0.00000 | $0.00630 |
| Opus 5.5 | $0.00000 | $0.00252 |
| Sonnet 5.5 | $0.00000 | $0.00126 |
| Haiku 4.5 | $0.00000 | $0.00063 |
Grade A, and why
llm-redteam scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/llm-redteam
Automated LLM/agentic red-teaming. Instead of hand-firing one prompt-injection at a time, this runs a categorized corpus against a chat endpoint and uses a canary token to reliably detect which payloads succeeded.
Usage
# simple endpoint that takes {"message": "..."}
/llm-redteam --url https://t/api/chat --field message
# only one category
/llm-redteam --url https://t/api/chat --field message --category jailbreak
# OpenAI-style body + nested response path
/llm-redteam --url https://t/api/chat \
--template '{"messages":[{"role":"user","content":"{{PAYLOAD}}"}]}' \
--response-path choices.0.message.content
Run directly:
tools/llm_redteam.py --url https://t/api/chat --field message --json
tools/llm_redteam.py --list-categories
Categories (OWASP LLM Top 10 / ASI)
| Category | Tests | OWASP |
|---|---|---|
prompt-injection |
direct instruction override | LLM01 |
jailbreak |
DAN / developer-mode persona escape | LLM01 |
system-prompt-leak |
extract the hidden system prompt | LLM07 |
data-exfil |
markdown-image beacon to attacker host | LLM02/LLM06 |
indirect-injection |
payload framed as a retrieved document | LLM01 (indirect) |
guardrail-bypass |
base64 / split-instruction filter evasion | LLM01 |
How detection works
Most payloads instruct the model to emit a unique token (RT_PWNED_xxxx). If
that token appears in the response, the injection landed — far more reliable
than keyword matching. System-prompt-leak uses a multi-signal heuristic;
data-exfil confirms when the canary URL is reflected in the output.
--header "Authorization: Bearer ..." (repeatable) for authed chatbots.
Turn a hit into a report
A bare prompt-injection is Informational until chained. Escalate:
injection → chatbot IDOR (read another user's data), data exfil (the
markdown-beacon hit proves a working channel), or RCE if the agent has a
code/tool execution capability. See skills/web2-vuln-classes §11 and
skills/bug-bounty Agentic AI (ASI01–ASI10).
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.
- 1mo ago First seen · 60 lines · 0 tokens per session scan A 5092b3da9e6e
llm-redteam is a command published in the GitHub repository Awarexone/Agentic-Bug-Hunter (5,367 stars, last pushed 11d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 630 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
recon
Run full recon pipeline on a target — subdomain enum (Chaos API + subfinder), live host discovery (dnsx + httpx), URL crawl (katana + waybackurls + gau), gf pattern classification, nuclei scan. Outputs to recon/ / directory. Usage: /recon target.com.
hunt
Active vulnerability hunting. Two-track dispatcher — asks Red Team vs WAPT, hands off to hunt-dispatch skill and sibling commands. Usage: /hunt target.com | /hunt .target.com | /hunt targets.txt [--vuln-class X] [--source-code P] [--chrome].
token-scan
Meme coin and token security scan — checks for rug pull vectors (hidden mint, honeypot, fee manipulation, LP lock bypass, authority retention, bonding curve exploits, fake renounce, sandwich amplification). Manual 8-class grep audit (with an optional automated scanner if present). Usage: /token-scan [--chain solana].
autopilot
Run autonomous hunt loop on a target — scope check → recon → rank surface → hunt → validate → report with configurable checkpoints. Usage: /autopilot target.com [--paranoid|--normal|--yolo].
triage
Quick 7-Question Gate triage on a finding before writing a report. Kills N/A submissions before they happen. Faster than /validate — for quick go/no-go decisions. Usage: /triage.
validate
Validate a finding — runs 7-Question Gate + 4-gate checklist. Kills weak findings before report writing. Prevents N/A submissions that hurt validity ratio. Usage: /validate.