mid-engagement-ir-detection
This Claude Code skill detects active security responses during authorized red-team engagements by comparing target state before and after testing, capturing evidence of real-time incident response such as patches, WAF deployments, or account lockouts. Use it when running active tests against monitored targets, a previously confirmed vulnerability stops reproducing, baseline timing shifts unexpectedly, or response patterns change during the engagement session.
git clone --depth 1 https://github.com/elementalsouls/Claude-BugHunter /tmp/mid-engagement-ir-detection && cp -r /tmp/mid-engagement-ir-detection/skills/mid-engagement-ir-detection ~/.claude/skills/mid-engagement-ir-detectionSKILL.md
## When to use this skill
Trigger when:
- Running active testing against a target with active SOC monitoring
- A confirmed-vulnerable finding stops reproducing on recheck
- Baseline timing shifts unexpectedly (3× slower, sudden errors, new headers)
- Response sizes change between test windows
- New WAF cookies or headers appear that weren't there at session start
- Lockout / error rates change between test windows (especially LOCKED count for credential attacks)
- Engagement is "assume breach" or "white box" — client knows you're testing
DO NOT use for:
- Bug bounty (client doesn't know you're there; no real-time IR)
- Pure recon (no state-change happening)
- One-off vulnerability scanning (no temporal dimension)
---
## The core insight
In a real red-team engagement against a competent SOC, the security state of the target is **not static**. It changes during your test in response to your traffic. These state changes are:
1. **Themselves valuable findings** (positive operational observations about IR responsiveness)
2. **Confirmation evidence** (mid-engagement patch = the original vulnerability was real)
3. **Classification signals** (WAF rule deployment vs code fix — different remediation depth)
Anti-pattern: treating reproduction failure as evidence the original signal was a false positive. **Original PoC artifacts captured before the change are still the vulnerability finding.**
---
## The discipline — capture before, diff after
### Before any active test:
```python
# Capture pre-test fingerprint of the target
fingerprint = {
"ts_pre": time.time(),
"ip_seen": "<operator-src-ip>",
"baseline_response_time_ms": <measure>,
"baseline_response_size_bytes": <measure>,
"response_headers": <capture set>,
"waf_cookies": <list>,
"lockout_count_in_state": <count from o365_attempts.json>,
}
```
Persist to `engagement_log/fingerprint_pre.json`.
### During the test:
Log every test result with full context (timestamp, IP, payload, response code, response size, response time, headers if relevant) to JSONL append-only.
### After the test session OR on first failed-recheck:
```python
fingerprint_post = same structure
delta = {
"baseline_time_change_ms": post.time - pre.time,
"baseline_size_change_bytes": post.size - pre.size,
"new_headers_appeared": post.headers - pre.headers,
"new_waf_cookies": post.cookies - pre.cookies,
"new_lockouts": post.locked_count - pre.locked_count,
}
```
If any delta is significant — **investigate, don't retract**.
---
## The three primary IR observations
### Observation 1 — Mid-engagement WAF rule deployment
**Symptoms:**
- Original payloads return identical response → no signal at all on recheck
- Body size identical to baseline (login page reflection)
- Timing reverts to baseline regardless of payload
- New cookie or header in responses (e.g., `cf-bm`, `__cf_bm`, `awselb`)
- Specific keyword in URL/body now triggers different response code (403, 406, 429)
**Confirmation:** retry with WAF-evasion variants:
- URL-encode the payload differently (`%27` vs `%5cu0027`)
- Change request method (POST → PUT, GET → POST)
- Different content-type (form-urlencoded → multipart)
- Slower pace (5s → 60s between requests)
- Mixed-case keywords (`SLEEP` → `SlEeP`)
If WAF-evasion variants restore the signal, the mitigation is at the WAF layer (bypassable).
If even WAF-evasion variants stay blocked, the mitigation is likely in code.
**Finding template:**
```
Subject: Mid-engagement mitigation deployed for <vulnerability X>
Observation: At engagement timestamp T0, vulnerability <X> on <endpoint> was
confirmed via <PoC>. At T0+<minutes>, recheck via the original payload no longer
reproduces the timing/error/size differential. <WAF-evasion variant> [does/does
not] restore the signal.
Description: This pattern is consistent with the client SOC observing engagement
traffic and deploying a mitigation in real time. Mitigation depth assessment:
[at-WAF, bypassable] vs [in-code, durable].
Impact (positive): Client SOC has both detection-grade visibility into application
traffic AND the authority to deploy mitigations within ~<minutes> of detection.
Impact (caveat): The original vulnerability did exist and was exploitable for at
least the engagement window before mitigation. If the mitigation is at the WAF
layer only, the underlying code-level flaw remains exploitable via alternative
payloads.
Recommendation:
1. Verify the mitigation is in code (parameterized queries, input sanitization),
not just at the WAF layer.
2. Audit the codebase for the same root cause across sister applications.
3. (Positive) Document the IR responsiveness as a capability metric.
```
### Observation 2 — Active concurrent attacker
**Symptoms:**
- Many `AADSTS50053` (LOCKED) responses despite your 1-attempt-per-user discipline
- Lockouts cluster alphabetically or by some other sort key
- New lockouts appear DURING your engagement (diff before/after)
- LOCKED rate exceeds expected baseline (in our engagement: 11% of all attempts → red flag)
**Math check:**
- Your discipline: 1 attempt per user lifetime
- Smart Lockout default: lockout after 10 failed attempts; lockout duration starts at 60 seconds and grows with each subsequent lockout (not a flat 10-minute window)
- Therefore: IF tool logs confirm exactly 1 attempt/user (no burst retries, no parallel-goroutine duplicate sends, no tool misconfiguration), you cannot mathematically cause Smart Lockout — verify `journal.jsonl` shows 1 attempt/user before asserting this
- Therefore: every NEW AADSTS50053 accumulating during the window (per before/after diff) was caused by someone else; pre-existing locks may stem from the legitimate user's own failures or a prior test run, so attribute only the newly-accumulating locks to an external party
**Confirmation:**
- Sort locked accounts alphabetically; if they cluster, attacker is using sorted username list
- Compare pre-session lockout count vs post-sessiEnd-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.