3 accepted papers
We introduce LLM Stinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional methods, which require complex prompt engineering or white-box access, LLM Stinger uses a reinforcement learning (RL) loop t…
We present a novel tool BertRLFuzzer, a BERT and Reinforcement Learning (RL) based fuzzer aimed at finding security vulnerabilities for Web applications. BertRLFuzzer works as follows: given a set of seed inputs, the fuzzer performs grammar-adhering and attack-provoking mutation operations on them t…
Modern SMT solvers, such as Z3, offer user-controllable strategies that allow solver users the ability to tailor solving strategies for their unique set of instances, thus dramatically enhancing the solver performance for their specific use cases. However, this approach of strategy customization pre…