# SoK: Sanitizing for Security

arXiv: https://arxiv.org/abs/1806.04355 · HTML: https://mlsanity.com/paper/1806.04355/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [A Survey on Failure Analysis and Fault Injection in AI Systems](https://mlsanity.com/paper/2407.00125/) (2024)
- [AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection](https://mlsanity.com/paper/2604.11950/) (2026)
- [C2RUST-BENCH: A Minimized, Representative Dataset for C-to-Rust Transpilation Evaluation](https://mlsanity.com/paper/2504.15144/) (2025)
- [LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software](https://mlsanity.com/paper/2604.12994/) (2026)
- [Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics](https://mlsanity.com/paper/2604.17715/) (2026)

## Abstract (arXiv)

The C and C++ programming languages are notoriously insecure yet remain indispensable. Developers therefore resort to a multi-pronged approach to find security issues before adversaries. These include manual, static, and dynamic program analysis. Dynamic bug finding tools --- henceforth "sanitizers" --- can find bugs that elude other types of analysis because they observe the actual execution of a program, and can therefore directly observe incorrect program behavior as it happens. A vast number of sanitizers have been prototyped by academics and refined by practitioners. We provide a systematic overview of sanitizers with an emphasis on their role in finding security issues. Specifically, we taxonomize the available tools and the security vulnerabilities they cover, describe their performance and compatibility properties, and highlight various trade-offs.