Fault injection
AssessTechniques
A testing technique that intentionally introduces failures to validate system resilience.
Why it's here
Placed in Assess: 5 article(s) of evidence from 3 source(s), led by research-stage coverage, with 0 in the last 30 days. Confidence 59%.
Evidence (5)
- 4Hacker News·8/27/2026securityFFmpeg division-by-zero bug found with a vibecoded fuzzer
A Hacker News post reports that a division-by-zero bug was discovered in FFmpeg using a fuzzer described as vibecoded. The item highlights automated fuzzing as the method used to trigger the issue, but provides no further technical details about the vulnerability or its impact.
- 7The New Stack·8/19/2026researchCanonical tests AI-assisted C-to-Rust translation on security-critical code
Canonical is funding researchers at the University of Bristol to evaluate whether automated tools can translate large C codebases into Rust without changing behavior. The project focuses on security-sensitive components such as AppArmor and snap-confine, and combines language-model code generation with fuzzing, formal analysis, and symbolic repair to verify equivalence and reduce unsafe usage.
- 5InfoQ·7/10/2026researchChaos Engineering for GPU Clusters
Bryan Oliver presents practical approaches to chaos engineering for large-scale GPU clusters used in AI infrastructure. The talk focuses on handling complex topologies, RDMA networking, NUMA misalignment, and fault injection to improve hardware efficiency and observability.
- 6Hacker News·7/4/2026researchAgentic coding, testing, and the risks of synthetic repros
The post describes hands-on experience using AI coding agents for debugging and testing, including a case where an agent produced a convincing but fabricated reproduction of a bug in a fake browser environment. It argues that agentic workflows can be powerful for testing and triaging issues, but also prone to errors, hallucinations, and misleading outputs that require human verification.
- 5The New Stack·6/24/2026researchCoder Benchmarks Anthropic's Mythos on Security Bug Detection
Software developer Joe Cooper created a benchmark to test whether Anthropic’s Mythos model can identify and explain hard security bugs in code repositories without prior hints. His findings aim to compare real-world debugging capability across models, especially for multi-file vulnerabilities and blind bug detection. The piece also notes Anthropic’s broader claims that Mythos can find and exploit zero-day vulnerabilities in major operating systems and browsers.