skwaq

Reference guide for LLM-based vulnerability detection in binary code. Provides best practices, techniques, and prompting strategies for using LLMs to find vulnerabilities in stripped binaries, firmware, and decompiled code. Use when analyzing binaries with AI, writing vulnerability analysis prompts, or optimizing detection pipelines.

Comprehensive binary security assessment including hardening, attack surface, and vulnerability analysis. Use when analyzing an ELF or PE binary.

Compare two binaries for security differences. Use for patch analysis or regression testing.

Look up CWE entries and explain their relevance. Use when discussing vulnerability types or CWE classifications.

Explain a specific vulnerability finding in detail with remediation guidance. Use when the user wants to understand a finding or how to fix it.

Multi-language source code security audit. Use when analyzing source code for vulnerabilities. Supports Python, JavaScript, Go, Rust, Java, C/C++.

Generate a threat model for the analyzed target. Use after ingesting a binary or source code to understand the threat landscape.

Run a comprehensive vulnerability scan on the current investigation. Use when the user wants to find security vulnerabilities in ingested code or binaries. Combines pattern detection, data flow analysis, and AI reasoning.