ai-ml-attacks
This skill should be used when the user asks about "AI security", "ML pipeline attacks", "prompt injection", "model…
This skill should be used when analyzing multi-service architectures, frontend-backend interactions, microservices, or when looking for vulnerabilities that span multiple components. Use when the user asks about "cross-component", "frontend to backend", "service interaction",
Claude Code
$ npx -y skills add allsmog/vuln-scout --skill cross-component --agent claude-code
Codex
$ npx skills add allsmog/vuln-scout --skill cross-component -a codex -y
Cursor
$ npx skills add allsmog/vuln-scout --skill cross-component -a cursor -y
Gemini CLI
$ npx skills add allsmog/vuln-scout --skill cross-component -a gemini-cli -y
GitHub Copilot
$ npx skills add allsmog/vuln-scout --skill cross-component -a github-copilot -y
OpenCode
$ npx skills add allsmog/vuln-scout --skill cross-component -a opencode -y
Windsurf
$ npx skills add allsmog/vuln-scout --skill cross-component -a windsurf -y
name: cross-component description: This skill should be used when analyzing multi-service architectures, frontend-backend interactions, microservices, or when looking for vulnerabilities that span multiple components. Use when the user asks about "cross-component", "frontend to backend", "service interaction", "internal service access", or "multi-service vulnerabilities".
Analyze how components interact and identify vulnerabilities that span service boundaries.
Single-component analysis misses:
# Docker Compose find . -name "docker-compose*.yml" -o -name "docker-compose*.yaml" # Supervisord find . -name "supervisord.conf" -o -name "supervisor*.conf" # Kubernetes find . -name "*.k8s.yaml" -o -name "deployment*.yaml" -o -name "service*.yaml" # PM2 find . -name "ecosystem.config.js" -o -name "pm2*.json"
# From docker-compose: services, ports, networks grep -E "^\s+\w+:|ports:|expose:|depends_on:|networks:" docker-compose.yml # From supervisord: programs and commands grep -E "^\[program:|command=" supervisord.conf # Internal-only services (not exposed externally) grep -E "127\.0\.0\.1:|localhost:" docker-compose.yml supervisord.conf
## Service Topology | Service | Port | Exposed | Language | Entry Points | |---------|------|---------|----------|--------------| | frontend | 1337 | ✅ External | Next.js | / | | backend | 3000 | ❌ Internal | Flask | /api/* | | redis | 6379 | ❌ Internal | - | - | ## Network Diagram
Internet → [1337] Next.js → [3000] Flask → [6379] Redis ↓ (Host header SSRF possible)
# Does backend trust frontend headers? grep -rn "X-User-ID\|X-Auth\|X-Forwarded\|X-Real-IP" --include="*.py" --include="*.go" # Does backend skip auth for internal requests? grep -rn "localhost\|127\.0\.0\.1\|internal" --include="*.py" --include="*.go" | grep -i "auth\|skip\|bypass"
# Find internal API calls grep -rn "fetch\|requests\.\|http\.get" --include="*.ts" --include="*.py" | grep -E "localhost|127\.0\.0\.1|internal" # Check for hardcoded service tokens grep -rn "SERVICE_TOKEN\|INTERNAL_API_KEY\|X-Internal" --include="*.ts" --include="*.py" --include="*.env*"
**Pattern**: External service has SSRF, internal service has high-impact vulnerability
# Step 1: Find SSRF in external service grep -rn "redirect\|fetch\|requests\." --include="*.ts" --include="*.tsx" front-end/ # Step 2: Find sinks in internal service grep -rn "render_template_string\|eval\|exec\|deserialize" --include="*.py" backend/ # Step 3: Check if SSRF can reach internal sink # - Same network/container? # - Internal port accessible? # - User input reaches sink?
**Pattern**: Frontend sets headers that backend trusts blindly
# Frontend setting user headers grep -rn "X-User\|X-Auth\|headers.*user" --include="*.ts" front-end/ # Backend trusting headers grep -rn "request\.headers\[" --include="*.py" backend/ | grep -v "Authorization"
**Pattern**: Frontend and backend share DB, one has SQLi
# Find database connection strings grep -rn "DATABASE_URL\|POSTGRES\|MYSQL\|MONGO" --include="*.env*" # If same DB, check both services for injection grep -rn "query\|execute\|rawQuery" --include="*.ts" --include="*.py"
## Attack Chain: [Name] **Severity**: CRITICAL/HIGH/MEDIUM **Components**: 1. [Service A] - [Vulnerability] at [location] 2. [Service B] - [Vulnerability] at [location] **Attack Flow**: 1. Attacker sends request to [external service] 2. [Vulnerability 1] causes [effect] 3. [Effect] reaches [internal service] 4. [Vulnerability 2] is triggered 5. Impact: [RCE/Data Breach/etc.] **Evidence**: - `frontend/serverActions.tsx:15` - redirect() with Host header control - `backend/routes.py:87` - render_template_string() with user input - `supervisord.conf` - both services on same network
Next.js (port 1337, external)
│
│ Server Action redirect()
│ + attacker Host header
▼
Attacker Server
│
│ 302 Redirect to internal
▼
Flask (port 3000, internal)
│
│ render_template_string()
│ with user input
▼
RCE via Jinja2 SSTIReact (external)
│
│ GraphQL query
▼
Express (external)
│
│ NoSQL injection in query
▼
MongoDB
│
│ Data exfiltration
▼
Credential theftAPI Gateway (external)
│
│ Message to queue
▼
Message Queue (internal)
│
│ Deserialization
▼
Worker Service (internal)
│
│ Command injection
▼
RCEAI-powered whitebox penetration testing plugin for Claude Code. 9 languages, 22 skills, 7 autonomous agents. STRIDE threat modeling, OWASP 2025 coverage, polyglot monorepo support.
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