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Vulnhunter

Organization
sendaifun
vulnhunter

Security vulnerability detection and variant analysis skill. Use when hunting for dangerous APIs, footgun patterns, error-prone configurations, and vulnerability variants across codebases. Combines sharp edges detection with variant hunting methodology.

Overview

Publishersendaifun
Repositoryskills
Skill namevulnhunter
Stars
128
Forks
81
Bundled files
6
LicenseApache-2.0
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.

  • 6 bundled files

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

  • Open source

    Published by sendaifun on GitHub. Read the source before you install it.

Installation

Install the Vulnhunter 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/sendaifun/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/vulnhunter .claude/skills/vulnhunter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vulnhunter 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 Vulnhunter 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 Vulnhunter 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.

VulnHunter - Security Vulnerability Detection & Analysis

A comprehensive security audit skill for identifying dangerous APIs, footgun patterns, error-prone configurations, and hunting for vulnerability variants across codebases. Inspired by Trail of Bits' sharp-edges and variant-analysis methodologies.

Overview

VulnHunter combines two powerful security analysis techniques:

  1. Sharp Edges Detection - Identify error-prone APIs, dangerous defaults, and footgun designs
  2. Variant Analysis - Find similar vulnerabilities across codebases using pattern-based analysis

When to Use VulnHunter

Activate this skill when:

  • Conducting security code reviews or audits
  • Reviewing third-party dependencies for dangerous patterns
  • Hunting for variants of known vulnerabilities
  • Assessing API design for security footguns
  • Pre-audit reconnaissance of unfamiliar codebases

Sharp Edges Detection

Categories of Sharp Edges

1. Dangerous Default Configurations

Look for configurations that are insecure by default:

- CORS: Access-Control-Allow-Origin: *
- Debug modes enabled in production
- Default credentials or API keys
- Permissive file permissions (777, 666)
- SSL/TLS verification disabled
- Insecure deserialization settings
2. Error-Prone APIs

Memory Safety:

c
// Dangerous: No bounds checking
strcpy(), strcat(), sprintf(), gets()
memcpy() without size validation

// Safer alternatives
strncpy(), strncat(), snprintf(), fgets()
memcpy_s() with explicit size

Cryptography Footguns:

- ECB mode encryption
- MD5/SHA1 for security purposes
- Hardcoded IVs or salts
- Custom crypto implementations
- Random without CSPRNG (Math.random for tokens)

Concurrency Issues:

- Race conditions in file operations
- Time-of-check to time-of-use (TOCTOU)
- Double-checked locking anti-patterns
- Non-atomic increment/decrement operations
3. Language-Specific Footguns

JavaScript/TypeScript:

javascript
// Dangerous patterns
eval(), new Function(), setTimeout(string)
innerHTML, outerHTML, document.write()
Object.assign() for deep clone (shallow only!)
== instead of === (type coercion)

Python:

python
# Dangerous patterns
pickle.loads(untrusted)  # RCE vector
yaml.load(untrusted)     # Use safe_load
exec(), eval()
os.system(), subprocess with shell=True

Rust:

rust
// Patterns requiring extra scrutiny
unsafe { }
.unwrap() in production code
mem::transmute()
raw pointer dereference

Solidity/Smart Contracts:

solidity
// High-risk patterns
tx.origin for authentication  // Phishing vulnerable
delegatecall to untrusted     // Storage collision
selfdestruct                  // Permanent destruction
block.timestamp for randomness // Miner manipulable

Sharp Edges Checklist

When reviewing code, systematically check for:

  • Authentication bypasses - Missing auth checks, default credentials
  • Authorization flaws - Privilege escalation, IDOR patterns
  • Injection vectors - SQL, Command, Template, XSS
  • Cryptographic weaknesses - Weak algorithms, improper key handling
  • Resource exhaustion - Unbounded loops, memory allocation
  • Race conditions - TOCTOU, concurrent state modification
  • Information disclosure - Verbose errors, debug endpoints
  • Deserialization - Untrusted data unmarshaling
  • Path traversal - User-controlled file paths
  • SSRF vectors - User-controlled URLs, redirects

Variant Analysis

The Variant Hunting Process

  1. Identify the Root Cause - Understand WHY a vulnerability exists
  2. Extract the Pattern - What code structure enables it?
  3. Generalize the Pattern - Create regex/AST patterns
  4. Search Codebase - Hunt for similar structures
  5. Validate Findings - Confirm each variant is exploitable

Pattern Extraction Templates

Template 1: Missing Validation Pattern
Original bug: User input flows to SQL query without sanitization
Pattern: [user_input] -> [sink_function] without [validation_function]

Search for:
- Direct database calls with string concatenation
- ORM raw query methods with user parameters
- Similar data flows in adjacent modules
Template 2: Authentication Bypass
Original bug: Endpoint missing auth middleware
Pattern: Route definition without auth decorator/middleware

Search for:
- Routes defined after the vulnerable one
- Similar API patterns in other modules
- Admin/internal endpoints
Template 3: Race Condition
Original bug: Check-then-act without atomicity
Pattern: if (check_condition()) { act_on_condition() }

Search for:
- File existence checks followed by file operations
- Permission checks followed by privileged actions
- Balance checks followed by transfers

Search Strategies

Grep-Based Search
bash
# Find potential SQL injection
grep -rn "execute.*%s" --include="*.py"
grep -rn "query.*\+" --include="*.js"

# Find dangerous deserialize
grep -rn "pickle.loads\|yaml.load\|eval(" --include="*.py"

# Find command injection vectors
grep -rn "os.system\|subprocess.*shell=True" --include="*.py"
Semantic Search (AST-Based)

For more precise matching, use AST-based tools:

  • Semgrep - Cross-language semantic grep
  • CodeQL - GitHub's semantic analysis
  • tree-sitter - Universal parser

Variant Analysis Report Template

markdown
## Variant Analysis Report

### Original Finding
- **ID**: FINDING-001
- **Severity**: High
- **Root Cause**: [Description]
- **Affected File**: path/to/file.ext:line

### Pattern Extracted
[Code pattern or regex]

### Variants Discovered

| # | Location | Severity | Status | Notes |
|---|----------|----------|--------|-------|
| 1 | file.ext:42 | High | Confirmed | Same root cause |
| 2 | other.ext:100 | Medium | Suspected | Needs validation |

### Recommendations
[Systematic fix approach]

Workflow

Phase 1: Reconnaissance

  1. Identify technology stack and languages
  2. Map entry points (APIs, CLI, file inputs)
  3. Locate authentication/authorization logic
  4. Find cryptographic operations
  5. Identify external integrations

Phase 2: Sharp Edges Scan

  1. Run through sharp edges checklist
  2. Focus on security-critical paths
  3. Document all suspicious patterns
  4. Cross-reference with known CVEs

Phase 3: Variant Hunting

  1. For each finding, extract pattern
  2. Search for variants systematically
  3. Validate each potential variant
  4. Assess aggregate risk

Phase 4: Reporting

  1. Consolidate findings by category
  2. Assign severity ratings
  3. Provide remediation guidance
  4. Highlight systemic issues

Integration with Static Analysis

Semgrep Rules for Common Patterns

yaml
# Example: Detect SQL injection in Python
rules:
  - id: sql-injection-format
    patterns:
      - pattern: $CURSOR.execute($QUERY % ...)
    message: "Potential SQL injection via string formatting"
    severity: ERROR
    languages: [python]

CodeQL Queries

ql
// Find tainted data flowing to dangerous sinks
import python
import semmle.python.dataflow.TaintTracking

from DataFlow::PathNode source, DataFlow::PathNode sink
where TaintTracking::localTaint(source.getNode(), sink.getNode())
  and sink.getNode().asExpr().(Call).getTarget().getName() = "execute"
select sink, source, sink, "Tainted input reaches SQL execution"

Examples

See the /examples folder for:

  • Real-world sharp edges examples by language
  • Variant analysis case studies
  • Pattern extraction walkthroughs

Resources

  • resources/sharp-edges-catalog.md - Comprehensive catalog of dangerous patterns
  • resources/variant-patterns.md - Common vulnerability pattern templates
  • templates/variant-report.md - Report template for variant analysis

Guidelines

  1. Always verify - Don't report theoretical issues as confirmed vulnerabilities
  2. Context matters - A pattern may be safe in one context, dangerous in another
  3. Prioritize exploitability - Focus on patterns that lead to real impact
  4. Document assumptions - Note any threat model assumptions
  5. Systemic over point fixes - Recommend architectural improvements when patterns repeat

Skill Files

vulnhunter/
├── SKILL.md                          # This file
├── resources/
│   ├── sharp-edges-catalog.md        # Categorized dangerous patterns
│   └── variant-patterns.md           # Vulnerability pattern templates
├── examples/
│   ├── smart-contracts/              # Solidity/blockchain examples
│   ├── web-apps/                     # Web application examples
│   └── native-code/                  # C/C++/Rust examples
├── templates/
│   └── variant-report.md             # Analysis report template
└── docs/
    └── methodology.md                # Detailed methodology guide

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 Vulnhunter AI skill do?

Security vulnerability detection and variant analysis skill. Use when hunting for dangerous APIs, footgun patterns, error-prone configurations, and vulnerability variants across codebases. Combines sharp edges detection with variant hunting methodology.

Why use Vulnhunter on TypingMind?

Because you install it once and use it with any model. Vulnhunter 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 Vulnhunter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sendaifun/skills/tree/main/skills/vulnhunter. 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 Vulnhunter?

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 Vulnhunter?

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

Is the Vulnhunter AI skill free?

Yes. It is published on GitHub by sendaifun under the Apache-2.0 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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