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Pentest Validation

Community
proffesor-for-testing
pentest-validation

Use when validating security findings from SAST/DAST scans, proving exploitability of reported vulnerabilities, eliminating false positives, or running the 4-phase pentest pipeline (recon, analysis, validation, report).

Overview

Publisherproffesor-for-testing
Repositoryagentic-qe
Skill namepentest-validation
Stars
480
Forks
92
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by proffesor-for-testing on GitHub. Read the source before you install it.

Installation

Install the Pentest Validation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git /tmp/agentic-qe
mkdir -p .claude/skills
cp -r /tmp/agentic-qe/assets/skills/pentest-validation .claude/skills/pentest-validation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pentest Validation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pentest Validation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pentest Validation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Pentest Validation

<default_to_action> When validating security findings:

  1. REQUIRE explicit authorization for target URL
  2. SCAN with qe-security-scanner (SAST + dependency + secrets)
  3. ANALYZE with qe-security-reviewer + qe-security-auditor (parallel)
  4. VALIDATE with qe-pentest-validator (graduated exploitation, parallel per vuln type)
  5. REPORT only confirmed findings with PoC evidence ("No Exploit, No Report")
  6. UPDATE exploit playbook with new patterns

Quality Gates:

  • Authorization confirmed before ANY exploitation
  • Target URL is staging/dev (NOT production)
  • Budget cap enforced ($15 default)
  • Time cap enforced (30 min default)
  • All exploitation attempts logged </default_to_action>

Quick Reference Card

The 4-Phase Pipeline

PhaseAgent(s)PurposeParallelism
1. Reconqe-security-scannerSAST, DAST, dependency scan, secretsInternal parallel
2. Analysisqe-security-reviewer + qe-security-auditorCode review + compliance checkBoth in parallel
3. Validationqe-pentest-validatorGraduated exploit validationPer-vuln-type parallel
4. Reportqe-quality-gate"No Exploit, No Report" filterSequential

Graduated Exploitation Tiers

TierHandlerCostLatencyUse When
1Agent Booster (WASM)$0<1msCode pattern is conclusive (eval, innerHTML, hardcoded creds)
2Haiku$0.0002~500msNeed payload test against live target
3Sonnet/Opus$0.003-$0.0152-5sFull exploit chain with data proof

When to Use This Skill

ScenarioTierEstimated Cost
PR security review (source only)1$0
Pre-release validation (staging)1-2$1-5
Full pentest validation1-3$5-15
Compliance audit evidence1-3$5-15

Configuration

yaml
pentest:
  target_url: https://staging.app.com    # REQUIRED for Tier 2-3
  source_repo: ./src                      # REQUIRED for Tier 1+
  exploitation_tier: 2                    # 1=pattern-only, 2=payload-test, 3=full-exploit
  vuln_types:                             # Which pipelines to run
    - injection                           # SQL, NoSQL, command injection
    - xss                                 # Reflected, stored, DOM XSS
    - auth                                # Auth bypass, session, JWT
    - ssrf                                # URL scheme abuse, metadata
  max_cost_usd: 15                        # Budget cap per run
  timeout_minutes: 30                     # Time cap per run
  require_authorization: true             # MUST confirm target ownership
  no_production: true                     # Block production URLs
  production_patterns:                    # URL patterns to block
    - "*.prod.*"
    - "api.*"
    - "www.*"

Safeguards (Mandatory)

Authorization Gate

Every pentest validation run MUST:

  1. Display target URL and exploitation tier to user
  2. Require explicit confirmation: "I own/authorized testing of this target"
  3. Log authorization with timestamp
  4. Block if target URL matches production patterns

What This Skill Does NOT Do

  • Full autonomous reconnaissance (Nmap, Subfinder)
  • Zero-day exploit development
  • Attack targets without explicit authorization
  • Test production systems
  • Store actual exfiltrated data (only proof of access)
  • Social engineering or phishing simulation
  • Port scanning or service discovery

Validation Pipelines

Injection Pipeline

AttackTier 1 (Pattern)Tier 2 (Payload)Tier 3 (Full)
SQL injectionString concat in query' OR '1'='1 response diffUNION SELECT data extraction
NoSQL injection$where, $gt in queryOperator injection testCollection enumeration
Command injectionexec(), system() callsCommand delimiter testReverse shell proof
LDAP injectionString concat in filterWildcard injectionDirectory enumeration

XSS Pipeline

AttackTier 1 (Pattern)Tier 2 (Payload)Tier 3 (Full)
Reflected XSSNo output encoding<img onerror> reflectionBrowser JS execution via qe-browser (Vibium)
Stored XSSinnerHTML assignmentPayload stored + retrievedCookie theft PoC
DOM XSSdocument.write(location)Fragment injectionDOM manipulation proof

Auth Pipeline

AttackTier 1 (Pattern)Tier 2 (Payload)Tier 3 (Full)
JWT noneNo algorithm validationModified JWT acceptedAdmin access with forged token
Session fixationNo session rotationPre-set session reusedCross-user session hijack
Credential stuffingNo rate limiting100 attempts unblockedValid credential discovery
IDORNo authorization checkAccess other user dataFull CRUD on foreign resources

SSRF Pipeline

AttackTier 1 (Pattern)Tier 2 (Payload)Tier 3 (Full)
Internal URLUser-controlled URL fetchhttp://169.254.169.254Cloud metadata extraction
DNS rebindingURL validation bypassRebind to internal IPInternal service access
Protocol smugglingURL scheme not restrictedfile:///etc/passwdFile content in response

Agent Coordination

Orchestration Pattern

typescript
// Phase 1: Recon (parallel scans)
await Task("Security Scan", {
  target: "./src",
  layers: { sast: true, dast: true, dependencies: true, secrets: true }
}, "qe-security-scanner");

// Phase 2: Analysis (parallel review)
await Promise.all([
  Task("Code Security Review", {
    findings: phase1Results,
    depth: "comprehensive"
  }, "qe-security-reviewer"),

  Task("Compliance Audit", {
    findings: phase1Results,
    frameworks: ["owasp-top-10"]
  }, "qe-security-auditor")
]);

// Phase 3: Validation (graduated exploitation)
await Task("Exploit Validation", {
  findings: [...phase1Results, ...phase2Results],
  target_url: "https://staging.app.com",
  exploitation_tier: 2,
  vuln_types: ["injection", "xss", "auth", "ssrf"],
  max_cost_usd: 15,
  timeout_minutes: 30
}, "qe-pentest-validator");

// Phase 4: Report ("No Exploit, No Report" gate)
await Task("Security Quality Gate", {
  findings: phase3Results.confirmedFindings,
  gate: "no-exploit-no-report",
  require_poc: true
}, "qe-quality-gate");

Finding Classification

StatusMeaningAction
confirmed-exploitableExploitation succeeded with PoCReport with evidence
likely-exploitablePartial exploitation, defenses detectedReport with caveats
not-exploitableAll exploitation attempts failedFilter from report
inconclusiveWAF/defense blocked, unclear if vulnerableReport for manual review

Exploit Playbook Memory

Namespace Structure

aqe/pentest/
 playbook/
  exploit/{vuln_type}/{tech_stack}/{technique}
  bypass/{defense_type}/{technique}
  payload/{vuln_type}/{variant}
 results/
  validation-{timestamp}
 poc/
  {finding_id}-poc

Learning Loop

  1. Before validation: Query playbook for known patterns matching findings
  2. During validation: Try known payloads first (higher success rate)
  3. After validation: Store new successful patterns with confidence scores
  4. Over time: Agent converges on most effective payloads per tech stack

Cost Optimization

Estimated Cost by Scenario

ScenarioTier MixFindingsEst. CostEst. Time
PR check (source only)100% Tier 15$0<5s
Sprint validation70% T1, 30% T215$2-55-10 min
Release validation40% T1, 40% T2, 20% T325$8-1515-30 min
Full pentest20% T1, 30% T2, 50% T340$15-3030-60 min

Cost vs Shannon Comparison

MetricShannonAQE Pentest Validation
Cost per run~$50$5-15 (graduated tiers)
Runtime60-90 min15-30 min (parallel pipelines)
False positive rateLow (exploit-proven)Low (same principle)
LearningNone (static prompts)ReasoningBank playbook

Success Metrics

MetricTargetMeasurement
False positive reduction>60% of findings eliminatedPre/post validator comparison
Exploit confirmation rate>80% of confirmed findings truly exploitableManual PoC verification
Cost per run<$15 USDToken tracking per pipeline
Time per run<30 minutesExecution time metrics
Playbook growth100+ patterns after 6 monthsMemory namespace count

Related Skills


Remember

"No Exploit, No Report." A vulnerability scanner that can't prove exploitation delivers uncertain value. This skill transforms security findings from theoretical risks into proven vulnerabilities with evidence. Every confirmed finding comes with a reproducible proof-of-concept. Every false positive is eliminated before it reaches the report.

Think proof, not prediction. Don't report what MIGHT be vulnerable. Prove what IS vulnerable.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pentest Validation AI skill do?

Use when validating security findings from SAST/DAST scans, proving exploitability of reported vulnerabilities, eliminating false positives, or running the 4-phase pentest pipeline (recon, analysis, validation, report).

Why use Pentest Validation on TypingMind?

Because you install it once and use it with any model. Pentest Validation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pentest Validation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/proffesor-for-testing/agentic-qe/tree/main/assets/skills/pentest-validation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pentest Validation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pentest Validation?

As many as you like. As long as a model supports skills, you can use Pentest Validation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pentest Validation AI skill free?

Yes. It is published on GitHub by proffesor-for-testing under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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