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Testing and Fuzzing the Kubernetes Admission Configuration


https://troopers.de/downloads/troopers23/TR23_TestingAndFuzzingTheKubernetes.pdf
Fuzz4All: Universal Fuzzing via Large Language Models

https://arxiv.org/pdf/2308.04748.pdf
ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP

https://arxiv.org/pdf/2308.02122.pdf
Chalk captures metadata at build time, and can add a small 'chalk mark' (metadata) to any artifacts, so they can be identified in production. Chalk can also extract chalk marks and collect additional metadata about the operating environment when it does this.

https://github.com/crashappsec/chalk
Fuzzer Development: The Soul of a New Machine

https://h0mbre.github.io/New_Fuzzer_Project/
Do Language Models Learn Semantics of Code? A Case Study in Vulnerability Detection

https://arxiv.org/pdf/2311.04109.pdf
FASER: Binary Code Similarity Search through the use of Intermediate Representations

https://arxiv.org/pdf/2310.03605.pdf
White-box Compiler Fuzzing Empowered by Large Language Models

https://arxiv.org/pdf/2310.15991.pdf
2025/06/30 13:17:39
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