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Vulnerability discovery, threat intelligence, reverse engineering, AppSec

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KernelGPT: Enhanced Kernel Fuzzing via Large Language Models

https://arxiv.org/pdf/2401.00563.pdf














GWP-ASan: Sampling-Based Detection of Memory-Safety Bugs in Production


This paper describes a family of tools that detect these two classes of memory-safety bugs, while running in production, at near-zero overhead. These tools combine page-granular guarded allocation and low-rate sampling. In other words, we added an “if” statement to a 36-year-old idea and made it work at scale

https://arxiv.org/pdf/2311.09394.pdf




White-box Compiler Fuzzing Empowered by Large Language Models

https://arxiv.org/pdf/2310.15991.pdf


FASER: Binary Code Similarity Search through the use of Intermediate Representations

https://arxiv.org/pdf/2310.03605.pdf


Do Language Models Learn Semantics of Code? A Case Study in Vulnerability Detection

https://arxiv.org/pdf/2311.04109.pdf




Fuzzer Development: The Soul of a New Machine

https://h0mbre.github.io/New_Fuzzer_Project/




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


OWASP Top 10 for LLM

https://llmtop10.com







Показано 20 последних публикаций.

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