hunt-brute-force
**hunt-brute-force** detects missing or ineffective rate limiting on authentication endpoints including OTP/2FA verification, password-reset tokens, login forms, and username enumeration vectors. Use this skill when testing whether applications can withstand brute-force attacks across a full keyspace (6-digit OTP = 1 million combinations) and whether attackers can bypass soft IP-throttling via header injection or identify valid accounts through timing or error-message differences. Distinguishes hard lockout, soft IP throttling, CAPTCHA injection, and silent shadow-throttling to avoid false-negative conclusions.
git clone --depth 1 https://github.com/elementalsouls/Claude-BugHunter /tmp/hunt-brute-force && cp -r /tmp/hunt-brute-force/skills/hunt-brute-force ~/.claude/skills/hunt-brute-forceSKILL.md
# HUNT-BRUTE-FORCE — Rate Limiting / Brute Force / Enumeration
> Grounding note: this skill is built from published technique classes, not from a
> curated set of named HackerOne reports. `report_count` is intentionally `0` — do
> not cite an exact payout or report ID you cannot verify. Where a public case is
> well-documented (e.g. Laxman Muthiyah's Instagram password-reset OTP race/rotation
> research, 2019–2021), it is named below as a *technique reference*, not a payout claim.
## Crown Jewel Targets
OTP brute force (6-digit = 1,000,000 combinations) with no effective rate limit = Critical ATO bypass.
**Highest-value chains:**
- **OTP / 2FA brute → MFA bypass → ATO** — no effective rate limit on `/verify-otp`, full 000000–999999 keyspace reachable
- **Password-reset token brute** — short/predictable/non-expiring tokens + no rate limit → ATO (the Instagram 2019 case combined a 6-digit reset code, no rate limit per request-source, and IP rotation to make 10^6 tractable)
- **Username/email enumeration → targeted credential stuffing** — valid/invalid distinguishable by response string, status code, or timing, then sprayed with breach corpora
- **Coupon / gift-card / referral code brute** — no rate limit on code validation → financial impact
- **ReDoS** — attacker-controlled input hits a catastrophic-backtracking regex → CPU exhaustion → DoS
---
## Autonomous Testing Priority
**Work within your turn budget — prioritize signal over volume.**
You cannot brute-force millions of combinations in automated testing. Focus on two things: (1) credential spraying with the most likely candidates, and (2) detecting whether rate limiting exists at all.
**Strategy:**
1. Identify the login endpoint and the expected parameter names (username/email, password).
2. Try weak/default credentials likely for the target context — default admin credentials for the app's stack, simple passwords for test environments, credentials visible elsewhere on the app (e.g. usernames exposed in profiles, default passwords in documentation).
3. After 3-5 failed attempts, check for rate-limit signals (429 status, "too many attempts" message, CAPTCHA appearance, account lockout message). Absence of these = rate limiting is missing = vulnerability.
4. Use form-encoding for traditional login forms, JSON for REST API login endpoints.
**What to look for as success:**
- Session token or JWT in the response body or Set-Cookie header
- Redirect to authenticated dashboard
- Response body that differs from the failed-login baseline
**Username enumeration (separate finding):** Try a known-valid username vs a random one. If the error message differs ("Wrong password" vs "User not found") or response time differs → user enumeration vulnerability, even without a successful login.
---
## CRITICAL: Four rate-limit states — do not collapse them
A `200`/`401` with no `429` does **not** mean "no rate limiting". A rate-limiting
skill that only checks for `429`/lockout produces false negatives. Classify the
defense BEFORE concluding, by sending a burst of ~50 requests and watching the
*full* response (status, body, headers, latency, and downstream success):
| State | Signal | Brute still feasible? |
|-------|--------|-----------------------|
| **Hard account lockout** | account disabled after N fails; later *correct* creds also fail | No (but lockout itself can be a DoS finding) |
| **Soft IP throttle** | `429` / increasing latency keyed on source IP only | Yes — bypass via header/IP rotation (Phase 4) |
| **CAPTCHA injection** | `200` but body switches to a CAPTCHA challenge after N | Maybe — check if the verify endpoint enforces it server-side or if the API path skips it |
| **Silent shadow-throttle** | `200`/`401` returned for every request, but submissions are *dropped* — the genuinely-correct OTP/password stops being accepted, or responses become canned | **This is the trap.** A naive loop sees "all 200, no 429" and reports "no rate limit" — false. |
**Shadow-throttle detector** — inject a known-good value at a known position and
confirm it still works under load:
```bash
# Seed: position 500 in the brute set is the REAL OTP for your own test account.
# If the loop reaches 500 and the correct code no longer authenticates,
# the endpoint is silently throttling/dropping — NOT unprotected.
KNOWN_GOOD="123456" # the actual current OTP for YOUR test account
for n in $(seq 0 600); do
CODE=$([ "$n" = "500" ] && echo "$KNOWN_GOOD" || printf "%06d" "$n")
CODE_RESP=$(curl -s -o /tmp/bf_body -w "%{http_code} %{time_total}" \
-X POST "https://$TARGET/api/verify-otp" \
-H "Content-Type: application/json" -H "Cookie: $SESSION_COOKIE" \
-d "{\"otp\":\"$CODE\"}")
echo "$n $CODE $CODE_RESP $(wc -c </tmp/bf_body)"
done
# Three columns to watch: status, time_total, body size.
# Rising time_total or a body-size change with status unchanged = shadow throttle.
```
---
## Step-by-Step Hunting Methodology
### Phase 1 — Login Rate Limit Test (classify, don't just count 429s)
```bash
# Send a burst and log status + latency + body length for EACH attempt.
for i in $(seq 1 50); do
read CODE TIME < <(curl -s -o /tmp/bf_l -w "%{http_code} %{time_total}\n" \
-X POST "https://$TARGET/api/login" \
-H "Content-Type: application/json" \
-d "{\"username\":\"test@$TARGET\",\"password\":\"wrong$i\"}")
echo "Attempt $i: status=$CODE time=${TIME}s len=$(wc -c </tmp/bf_l)"
sleep 0.1
done
# Then CLASSIFY against the 4-state table above. Watch for:
# - status flips to 429 / 403 → soft throttle or lockout
# - body grows / CAPTCHA token appears → CAPTCHA injection
# - latency climbs while status stays 401 → shadow throttle
# - genuinely nothing changes across all 50 → candidate "no rate limit" (confirm w/ Phase 2 seed)
```
### Phase 2 — OTP / 2FA Brute Force
```bash
# PRE-REQUISITE: a valid session that is pending OTP verification (your own test account).
SESSION_COOKIE="pre-auth-session-after-first-factor"
# ---- 2a. PEnd-to-end Android APK red-team pipeline — automated APK acquisition (Play Store + apkpure + apkmirror fallback), jadx decompilation, secret/URL/JWT/Firebase grep, pinned-cert extraction, exported-component enumeration, Frida runtime instrumentation templates, intent-injection probes. Built from an authorized external red-team engagement where 7 APKs were pulled manually, 4 download attempts truncated, and a hardcoded JWT + 30 internal API endpoints were recovered from one of the apps. Use when target has a mobile app catalogue (Play Store developer page), when you find an APK URL hosted on a web server, or when post-recon mentions "mobile app" in scope.
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]
Local-tooling companion to the bug-bounty orchestrator — carries the SAME complete bug-bounty workflow, but reach for THIS variant when you also need to resolve where tools, wordlists, and clones are installed on the local machine (jhaddix, SecLists, trufflehog, ffuf, dalfox, ghauri); for pure orchestration/routing use the bug-bounty skill. Workflow it covers — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation gates, human-tone writing, templates by vuln class, CVSS 3.1, PoC generation, always-rejected list, conditional chain table, submission checklist). Use when you need the local install path of a tool / wordlist / clone for a hunt, or as the full-workflow variant when operating from this local toolkit; for general routing use the bug-bounty skill. 中文触发词：漏洞赏金、安全测试、渗透测试、漏洞挖掘、信息收集、子域名枚举、XSS测试、SQL注入、SSRF、安全审计、漏洞报告
Use at the START of any bug bounty hunting session, when switching targets, or when feeling lost about what to do next. Master orchestrator that combines the 5-phase non-linear hunting workflow with the critical thinking framework (developer psychology, anomaly detection, What-If experiments). Routes to all other skills based on current hunting phase. Also use when asking "what should I do next" or "where am I in the process.
Complete bug bounty workflow — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation gates, human-tone writing, templates by vuln class, CVSS 3.1, PoC generation, always-rejected list, conditional chain table, submission checklist). Use for ANY bug bounty task — starting a new target, doing recon, hunting specific vulns, auditing source code, testing AI features, validating findings, or writing reports. 中文触发词：漏洞赏金、安全测试、渗透测试、漏洞挖掘、信息收集、子域名枚举、XSS测试、SQL注入、SSRF、安全审计、漏洞报告
Bugcrowd-specific reporting tactics complementing report-writing: VRT category search-and-fallback strategy when no exact match exists, manual severity override when VRT defaults underrate impact, severity-request paragraph as first body section, OOS-clause rebuttal templates (rate limiting on auth-flow endpoints, debug-info framing, user-enumeration with sensitive PII, theoretical-issue counter), chained-finding cross-reference patterns, target selection for QA-vs-prod programs, researcher-side hygiene (Bugcrowdninja email alias, account state restoration, friendly-tester posture). Use when filing a Bugcrowd submission, when VRT default seems wrong, when triager closes as OOS or downgrades severity, when chaining linked submissions, or when scope distinguishes production from QA. Pairs with report-writing and triage-validation.
Build an exploit chain — given bug A, finds B and C to combine for higher severity and payout. Knows common chain patterns: IDOR→ATO, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth. Usage: /chain
Cloud IAM red-team attack chain across AWS, Azure, GCP — focused on EXTERNAL exploitation paths and post-credential-discovery privilege analysis. Covers IAM enumeration (aws iam, az role, gcloud iam), STS/AssumeRole chaining, Azure Managed Identity abuse (via SSRF/leak), GCP service account JSON abuse, IMDSv1/v2 attacks via SSRF, K8s ServiceAccount token privilege analysis once held (token discovery / cluster exposure is owned by hunt-k8s), role-trust-policy confused-deputy, cross-account assume-role enumeration, IAM privilege escalation patterns (24+ AWS, 8+ Azure, 6+ GCP), and AWS Cognito Identity Pool unauthenticated-role attack chain (GetId → GetCredentialsForIdentity → IAM role abuse). Built for the case where recon yields a credential (key, JSON, token) and you need to know what it grants and how to escalate. Use when an AWS key / Azure secret / GCP service account JSON / K8s SA token surfaces from a code repo, JS bundle, APK, breach corpus, or SSRF chain.