Autokaker: When LLMs Hunt for Vulnerabilities in Your C Code
Hook
What if your security auditor was an AI that worked for free, analyzed code at machine speed, and could patch its own findings? Autokaker brings LLM-powered vulnerability discovery to your C projects—but the results might surprise you.
Context
Traditional static analysis tools like Coverity and Fortify rely on predefined rules and patterns to catch security vulnerabilities. They’re precise but brittle—they only find what they’ve been programmed to find. Meanwhile, manual code audits by security experts are thorough but expensive and slow, often costing thousands of dollars per project. The rise of large language models has opened a new possibility: what if we could combine the speed of automated analysis with the contextual reasoning of human reviewers?
Autokaker enters this gap as an experimental tool that applies LLMs to the vulnerability discovery pipeline. Built by the Neuroengine-vulns team, it attempts to automate two traditionally separate workflows: finding security flaws in codebases and generating patches to fix them. Unlike rule-based tools that search for known patterns, Autokaker uses models like Llama3 to analyze code semantics and identify potential vulnerabilities. It’s not trying to replace professional security audits—it’s trying to give developers a first-pass filter before code ever reaches a human reviewer.
Technical Insight
Autokaker operates in two distinct modes: vulnerability discovery and auto-patching. The architecture is refreshingly straightforward—a Python CLI and GUI wrapper around LLM API calls, with the real intelligence delegated to the models themselves.
For vulnerability discovery, you point autokaker at a single file or directory:
python autok.py source.cThe tool reads your source code, constructs a prompt asking the LLM to identify security vulnerabilities, and returns annotated findings. The examples in the README focus on C code, though language support beyond C is not explicitly documented.
The more interesting mode is auto-patching, which adds a feedback loop mechanism:
cd zlib
python ../autok.py --patch . --make "make"Here’s where Autokaker differentiates itself. The --patch flag activates remediation mode—the LLM not only identifies vulnerabilities but generates patches to fix them. The --make flag adds compilation validation: after applying a patch, Autokaker runs your specified build command and checks if the project still compiles.
You can chain commands for more sophisticated validation:
python ../autok.py --patch . --make "make&&./example64"This runs both compilation and a functional test (zlib’s example64 compression/decompression tool), creating a rudimentary feedback loop that catches patches which compile but break functionality.
The dual API approach is clever. Autokaker supports both OpenAI’s API (requiring an api-key.txt file) and Neuroengine.ai’s free API, which provides access to Llama3 and other open models without authentication. This removes the cost barrier for security researchers working on open-source projects or students learning vulnerability research. The GUI includes a model selector dropdown, letting you A/B test different models against the same codebase.
The repository includes a Crashbench V1 leaderboard showing performance metrics across different LLMs, providing transparency about which models perform better at vulnerability detection.
The tool accepts both file and directory paths as input, though the documentation doesn’t detail how it handles large codebases or what strategies it uses for analyzing multiple files.
Gotcha
LLMs hallucinate, and in security contexts, that’s dangerous. Autokaker will confidently flag false positives—vulnerabilities that don’t actually exist—because the model pattern-matches against what looks suspicious rather than performing rigorous data flow analysis. More concerning, it will miss real vulnerabilities that require deep semantic understanding or tracking data flow across multiple files and function calls.
The auto-patching mode is even riskier. An LLM-generated patch might fix the immediate vulnerability but introduce a new bug, break edge cases, or apply an incorrect security pattern. The README’s feedback loop using --make catches compilation failures, but it won’t catch subtle logic errors or performance regressions. A patch might fix a buffer overflow by adding a bounds check—but place it inside a hot loop, destroying your application’s performance. The build succeeds, basic tests pass, but production crashes.
The documentation is sparse. The README shows C code examples but doesn’t explicitly state language limitations, doesn’t explain configuration options, or detail how the tool handles large codebases. The repository has 68 stars—a small community means limited battle-testing and fewer people who’ve hit edge cases. You’re essentially beta testing someone’s research experiment.
Verdict
Use Autokaker if you’re doing security research on open-source projects (particularly C code based on the examples) and want a quick triage pass to identify areas worth manual investigation, or if you’re learning vulnerability discovery and want to see how LLMs reason about security flaws across different models using the free Neuroengine.ai API. It’s valuable as a discovery assistant, not an authority. Skip it if you need production-grade security analysis where false positives waste expensive human review time, if you’re working in languages where support is unclear, or if you’re considering applying auto-generated patches without thorough manual review. Never trust the auto-patcher in production codebases—treat its patches as suggestions that require validation by someone who understands both the codebase and security implications. This is a research tool, not a security product.
