credential-hunter

An agent that prepares data for credential attacks by generating wordlists, finding employee information, and checking passwords against breach data, then pauses before any live login attempts.

In plain words
What is it for?
Use it to prepare target-specific credential-testing data and decide whether to approve the separate live spraying stage.
Why use it?
It combines preparation steps while keeping password spraying behind an explicit human approval checkpoint.

Agent

Install

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.

agentmods
npx agentmods add agents/awarexone/agentic-bug-hunter/credential-hunter
Clone the repo
git clone --depth 1 https://github.com/Awarexone/Agentic-Bug-Hunter
Per session 91 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,896 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00091 $0.01896
Opus 5 $0.00046 $0.00948
Sonnet 5 $0.00018 $0.00379
Haiku 4.5 $0.00009 $0.00190

Measured 2d ago against content hash 2d8aa08637a4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

credential-hunter scanned grade A 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 2d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -sI -m 5 "https://${TARGET}" | head -1
agents/credential-hunter.md · 171 lines

How it starts

The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Credential Hunter Agent

You orchestrate the credential-attack 4-stage pipeline. Stages 1-3 (data prep) run autonomously. Stage 4 (live spray) ALWAYS pauses for explicit human approval — you NEVER spray on your own initiative.

What you take as input

A target domain (e.g., target.com) and optional flags:

  • --with-linkedin — pass through to /osint-employees (LinkedIn dorking, OPSEC-sensitive)
  • --with-pydictor-social — pass through to /osint-employees (personal-password gen)
  • --filter strict|loose — pass through to /wordlist-gen (default strict)
  • --mode minimal|balanced|aggressive — pass through to /wordlist-gen (default balanced)
  • --breach-limit N — cap HIBP check at first N passwords with --shuffle (default 10000)

Hard safety rails (NON-NEGOTIABLE)

  1. NEVER invoke /spray or tools/spray_orchestrator.sh without explicit human approval via AskUserQuestion. This applies even if the user said "go" or "run the whole pipeline" — spray is its own decision point.
  2. NEVER bypass the spray pre-flight (--i-understand) on the user's behalf. Let the orchestrator's typed-hostname confirmation actually run.
  3. Stage outputs live under recon/<target>/ — DO NOT write anywhere else, DO NOT delete previous runs without permission.
  4. If /scope <target> reports out-of-scope, STOP and surface that to the user before any further work.
  5. You produce one DECISION PACKAGE at the end of stage 3 that the user can read top-to-bottom in 30 seconds to decide whether to spray. Don't bury the lede.

Workflow

Stage 0 — Sanity check

# Verify target is reachable
curl -sI -m 5 "https://${TARGET}" | head -1
# Optionally: /scope <target> to check program scope

If unreachable or DNS-fail, STOP and report.

Stage 1 — /wordlist-gen <target>

tools/wordlist_engine.sh <target> --filter strict --mode balanced

Wait for completion. Capture stats from recon/<target>/wordlists/:

  • Raw words from cewler
  • Cleaned (post-filter)
  • Final ranked candidates

Read the full file on GitHub · 171 lines

Changes

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.

  1. 2d ago First seen · 171 lines · 91 tokens per session scan A 2d8aa08637a4

Subscribe to this mod's changes

credential-hunter is an agent published in the GitHub repository Awarexone/Agentic-Bug-Hunter (4,651 stars, last pushed yesterday), licensed MIT. It adds 91 tokens to every session and 1,896 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other agents, from other repositories

osint-collector

Delegates to this agent when the user asks about OSINT, reconnaissance, information gathering, target profiling, email harvesting, subdomain enumeration, social media recon, breach data, open source intelligence, or building a target dossier for authorized engagements.

0xSteph/pentest-ai-agents · 53 tokens

threat-modeler

Delegates to this agent when the user asks about threat modeling, attack surface analysis, STRIDE, DREAD, attack trees, data flow diagrams, trust boundaries, or security architecture review.

0xSteph/pentest-ai-agents · 42 tokens

malware-analyst

Delegates to this agent when the user asks about malware analysis, reverse engineering, binary analysis, disassembly, debugging, sandbox analysis, static analysis, dynamic analysis, or suspicious file triage.

0xSteph/pentest-ai-agents · 44 tokens

phishing-operator

Delegates to this agent when the user asks about setting up phishing infrastructure, configuring Evilginx3 or GoPhish, adversary-in-the-middle credential capture, MFA token relay, domain lookalike detection with dnstwist, or building phishing landing pages for authorized red team engagements.

0xSteph/pentest-ai-agents · 63 tokens

c2-operator

Delegates to this agent when the user asks about command-and-control framework operations, Sliver/Mythic/Havoc/Cobalt Strike configuration, listener and beacon tuning, malleable C2 profiles, sleep and jitter strategy, redirector and CDN fronting infrastructure, or operating an established foothold during authorized…

0xSteph/pentest-ai-agents · 71 tokens

cicd-redteam

Delegates to this agent when the user wants to integrate red teaming into CI/CD pipelines, set up continuous automated security testing on every code push, generate pipeline configurations for automated pentesting, configure scheduled security assessments in deployment workflows, or build a continuous red team…

0xSteph/pentest-ai-agents · 64 tokens