AI SAST: Code Security for the Agentic SDLC

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Endor Labs has launched an AI-powered Static Application Security Testing (SAST) tool for C code that detects more vulnerabilities than traditional scanners without requiring a software build. By combining deterministic program analysis with LLM reasoning, the tool identifies complex, multi-function data-flow flaws that often bypass conventional scanners, the company wrote in its announcement.

AI assistants are writing C faster than anyone can review it, but SAST scanners that have been used to catch bugs like integer and buffer overflows have struggled with C code.

Endor Labs’ AI SAST scans run faster than build-based SAST scans, and according to tests it has run, AI SAST caught 96 of 102 known vulnerabilities in four embedded C projects, which was 48 times the next best buildless pattern-based SAST tool, the company wrote. Endor’s tool is not based on patterns but rather it uses AI to reason about the code as a security engineer would, and the company said it outperformed four SAST tools and frontier models Claude and Codex in benchmark trials.

As Endor explained, “the gap comes down to how the two usual approaches work, and each one fails the opposite way. A frontier model pointed at a repo reasons well about the code it reads, but it only reads a slice. A pattern scanner reads every file but reasons about none of them, so it flags what code resembles rather than what it does, and buries you in false positives. AI SAST pairs deterministic program analysis (the same call-graph and reachability engine we built for SCA) with LLM reasoning.”

The program analysis maps the whole codebase and traces how data moves through it; the models reason over that structured context instead of raw text. You get coverage a model alone can’t reach, with less of the noise a pattern engine can’t help (see the whitepaper for more details on how AI SAST works).

How it handles what patterns can’t

We previously outlined the four structural reasons C breaks static analysis. Here’s how AI SAST answers each.

1. The analyzer never sees the code you wrote. Macros, #ifdefs, and per-config builds mean the code a traditional tool analyzes isn’t the code on disk, which is why those tools hook the compiler to reconstruct it. AI SAST reads and reasons about the source directly, so it doesn’t depend on reproducing one exact build to see what’s there.
2. Pointers defeat dataflow analysis. Instead of over-approximating into noise or under-approximating into missed bugs, AI SAST follows the data across functions and files and works out whether the length check three functions upstream actually bounds this copy. That’s the question that matters in C, and the one a rule can’t answer.
3. C’s bugs don’t fit pattern rules. Buffer overflows, use-after-free, integer overflows that feed an allocation size: these are about lengths, lifetimes, and arithmetic spanning functions, not the source-to-sink shapes a rule engine expresses well. AI SAST catches both the classic memory-safety bugs and the cross-function flaws that let an attacker take over the system.
4. There’s no framework to model. Every C codebase has its own allocators, string handling, and ownership conventions. AI SAST reads how your code actually manages memory rather than leaning on generic rules that miss what’s dangerous in your code and flag what isn’t.

Every finding comes with the same evidence it does in every other language: a call path, a working exploit, and a suggested fix.

Where it fits

AI SAST runs where C gets written, not as a gate at the end. A developer scans locally to check AI-generated C as they write it, and the same analysis runs on the pull request, so new flaws get found and fixed before they reach production instead of piling into a backlog. That keeps security in step with AI-accelerated development instead of turning review into the bottleneck.

C SAST pairs with C SCA in the same platform, so your first-party C and the open source it depends on are covered together. Both run on AURI by Endor Labs, our security harness for the agentic SDLC: an independent layer outside the coding agent (the model writing the code isn’t the only thing reviewing it), verifiable findings with function-level call paths and reproducible evidence, and policy you set once and enforce across any agent, model, or CI stage.

The next C file an agent edits was probably written before anyone on your team joined, and the agent will faithfully reproduce whatever habits it finds there. That’s the code this was built to review, while the PR is still open.

 

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