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By allsmog
Perform AI-powered whitebox penetration testing on polyglot monorepos across 9 languages: scan code with Semgrep/CodeQL/Joern, conduct STRIDE threat modeling, trace data flows to sinks, verify findings with agents, generate SARIF reports, and auto-apply fixes via commands.
npx claudepluginhub allsmog/vuln-scout --plugin whitebox-pentestAuto-remediate verified findings by generating patches and optionally creating a PR
Create a custom Semgrep detection rule from a confirmed vulnerability pattern
Compare security posture between two git refs to find new/fixed vulnerabilities and track regression
Security mutation testing -- weaken security controls and check if the scanner detects the resulting vulnerability
Pattern propagation - find all instances of a vulnerability pattern throughout the codebase
Use this agent when the user asks to "understand the application", "map the codebase", "analyze the architecture", "identify trust boundaries", "map user roles", or needs to build comprehensive application understanding before vulnerability hunting.
Autonomous attack vector exploration agent that hypothesizes novel attack vectors, tests them against the codebase, and iterates. Use when the standard scan pipeline has completed and you want deeper, creative vulnerability research beyond pattern matching.
Use this agent when the user asks to "review code for security", "find vulnerabilities", "security audit", "analyze for security issues", or when exploring a codebase with security concerns.
Use this agent to verify security findings and eliminate false positives. Analyzes code context, data flow paths, and exploitability with structured evidence to determine if a finding is a true positive or false positive.
Use this agent when the user wants to "test a vulnerability", "confirm exploitation", "debug the application", "verify the finding", or needs guidance on dynamic testing during Phase 2 of whitebox pentesting.
This skill should be used when the user asks about "AI security", "ML pipeline attacks", "prompt injection", "model deserialization", "unsafe model loading", "Jupyter injection", "LLM security", or needs to identify AI/ML-specific vulnerabilities in codebases that use machine learning frameworks.
This skill should be used when the user asks about "business logic", "workflow vulnerability", "trust boundary", "state machine", "authorization bypass", "multi-step process", "workflow bypass", "application logic flaw", or needs to identify business logic vulnerabilities during whitebox pentesting.
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This skill should be used when the user asks about "cloud security", "AWS security", "GCP security", "Azure security", "Kubernetes security", "IMDS", "instance metadata", "S3 bucket policy", "IAM", "serverless security", "Lambda security", "container security", "cloud misconfiguration", "SSRF to cloud metadata", or needs to identify cloud-native security issues during whitebox pentesting.
This skill should be used when the user asks about "compliance mapping", "PCI-DSS", "HIPAA", "SOC 2", "NIST CSF", "regulatory requirements", "compliance report", or needs to map security findings to compliance framework requirements.
Uses power tools
Uses Bash, Write, or Edit tools
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Security testing skills for AI-assisted IDEs and coding agents. 25 vulnerability patterns across code execution, prompt injection, data exfiltration, and trust persistence.
AI-powered cybersecurity code review with 8 specialist agents, OWASP Top 10:2021, CWE Top 25:2024, MITRE ATT&CK v15, and framework-aware false-positive suppression
Open-source cybersecurity analysis agent. Scans any local project for vulnerabilities: code security (SAST), dependency CVEs (SCA), secret leaks, authentication/authorization flaws, cryptographic weaknesses, misconfigurations, supply chain risks, and CI/CD security. Covers all OWASP 2025 Top 10 and CWE Top 25 categories. Generates prioritized reports with remediation guidance. Invoke with /cyber-neo [path].
Automated security vulnerability testing covering OWASP Top 10, SQL injection, XSS, CSRF, and authentication issues
Static analysis toolkit with CodeQL, Semgrep, and SARIF parsing for security vulnerability detection
Security scanning, dependency CVE audits, and exposure-aware risk prioritization.
AI-powered PASTA threat modeling plugin for Claude Code. Walks through the 7-stage Process for Attack Simulation and Threat Analysis with STRIDE classification, MITRE ATT&CK mapping, DFD generation, and evidence-anchored threat scenarios.
AI-first compliance assessment plugin for Claude Code. Runs prompt-orchestrated ISO 27001 Annex A and NIST SP 800-53 Rev 5 assessments with enterprise evidence packs, evidence-backed findings, cold review rounds, and durable run artifacts.
AI-powered whitebox penetration testing for Claude Code.
One command. Full audit. Any codebase.
/whitebox-pentest:full-audit /path/to/code
VulnScout is a Claude Code plugin that turns Claude into an autonomous security reviewer. It brings battle-tested pentesting methodology (HTB Academy, OffSec AWAE/OSWE) into your terminal with STRIDE threat modeling, evidence-first findings, and support for 9 languages including Solidity smart contracts.
Tested end-to-end on OWASP Juice Shop v17.1.1 -- 62 findings across SQL injection, XSS, path traversal, SSTI, SSRF, hardcoded secrets, and more.
Traditional SAST tools find patterns. VulnScout understands your application.
# Option 1: Symlink into your project's plugin directory
mkdir -p .claude/plugins
ln -s /path/to/vuln-scout/whitebox-pentest .claude/plugins/whitebox-pentest
# Option 2: Copy into your project
cp -r /path/to/vuln-scout/whitebox-pentest .claude/plugins/whitebox-pentest
# Run a full audit
/whitebox-pentest:full-audit .
# Or start with threat modeling
/whitebox-pentest:threats
Note:
.claude/plugins/is relative to your project root. Claude Code automatically discovers plugins in this directory.
VulnScout includes Python scripts that run independently of Claude Code:
# Scan with Semgrep + secret scanning
python3 scripts/scan_orchestrator.py /path/to/code --tools semgrep --secrets --format sarif
# Create a Joern CPG (cached by content hash)
python3 scripts/create_cpg.py /path/to/code
# Batch-verify findings with Joern CPG analysis
python3 scripts/batch_verify.py --findings .claude/findings.json --cpg .joern/*.cpg
# Render HTML or Markdown from an existing findings artifact
python3 scripts/report.py .claude/findings.json --format html --output security-report.html
# CI gate: fail on high-severity findings
python3 scripts/scan_orchestrator.py . --tools semgrep --fail-on high --format sarif --output findings.sarif
| Command | What it does |
|---|---|
/whitebox-pentest:full-audit | One command does everything -- scopes, threat models, audits, reports |
/whitebox-pentest:threats | STRIDE threat modeling with data flow diagrams |
/whitebox-pentest:sinks | Find dangerous functions across 9 languages |
/whitebox-pentest:trace | Follow data from source to sink |
/whitebox-pentest:scan | Run Semgrep, CodeQL, and Joern into a shared findings artifact |
/whitebox-pentest:scope | Handle large codebases with smart compression |
/whitebox-pentest:propagate | Found one bug? Find every instance of the pattern |
/whitebox-pentest:verify | CPG-based false positive elimination |
/whitebox-pentest:report | Render Markdown, JSON, SARIF, or HTML from the shared findings artifact |
/whitebox-pentest:diff | Compare security posture between git refs and highlight regressions |
/whitebox-pentest:auto-fix | Auto-remediate verified findings with generated patches |
/whitebox-pentest:create-rule | Generate a custom Semgrep rule from a confirmed vulnerability pattern |
/whitebox-pentest:mutate | Mutation-test security controls to find detection gaps |
Agents run independently and return detailed analysis: