Manual code analysis has always been a core component of any thorough security review. But especially with large embedded codebases containing hundreds of thousands of lines of C/C++, even an experienced team reaches time constraints. AI-assisted analysis tools promise relief – but how well do they actually work?
What AI Can Do Today
Modern Large Language Models have developed remarkable understanding of code semantics. In our practice, we use AI tools specifically for the following tasks:
- Triage and Prioritization: AI can quickly scan large codebases and identify suspicious patterns – buffer overflows, use-after-free, integer overflows
- Context Understanding: Unlike classical static analysis tools, LLMs grasp the semantic context of a function and can detect more complex vulnerability patterns
- Documentation: Automatic generation of vulnerability descriptions and recommendations saves significant time in reporting
Where the Limits Lie
AI does not replace manual analysis – it complements it:
- False Positives: AI tools produce significantly more false positives than an experienced analyst. Every finding must be manually verified
- Architecture Understanding: Complex vulnerabilities spanning multiple components require deep system understanding that AI cannot (yet) provide
- Hardware Context: Embedded-specific vulnerabilities such as timing side-channel attacks or DMA-based attacks lie beyond the AI horizon
Our Approach
At proofnet, we combine AI-assisted pre-analysis with deep manual expertise. AI handles the breadth search, our analysts handle the depth analysis. The result: higher coverage with the same quality.
Learn more about our Source Code Review offering or get in touch directly.