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Conference:  Defcon 31
Authors: Xavier Cadena

Large Language Models are already revolutionizing the software development landscape. As hackers we can only do what we've always done, embrace the machine and use it to do our bidding. There are many valid criticisms of GPT models for writing code like the tendency to hallucinate functions, not being able to reason about architecture, training done on amateur code, limited context due to token length, and more. None of which are particularly important when writing fuzz tests. This presentation will delve into the integration of LLMs into fuzz testing, providing attendees with the insights and tools necessary to transform and automate their security assessment strategies. The presentation will kick off with an introduction to LLMs; how they work, the potential use cases and challenges for hackers, prompt writing tips, and the deficiencies of current models. We will then provide a high level overview explaining the purpose, goals, and obstacles of fuzzing, why this research was undertaken, and why we chose to start with 'memory safe' Python. We will then explore efficient usage of LLMs for coding, and the Primary benefits LLMs offer for security work, paving the way for a comprehensive understanding of how LLMs can automate tasks traditionally performed by humans in fuzz testing engagements. We will then introduce FuzzForest, an open source tool that harnesses the power of LLMs to automatically write, fix, and triage fuzz tests on Python code. A thorough discussion on the workings of FuzzForest will follow, with a focus on the challenges faced during development and our solutions. The highlight of the talk will showcase the results of running the tool on the 20 most popular open-source Python libraries which resulted in identifying dozens of bugs. We will end the talk with an analysis of efficacy and question if we'll all be replaced with a SecurityGPT model soon. To maximize the benefits of this talk, attendees should possess a fundamental understanding of fuzz testing, programming languages, and basic AI concepts. However, a high-level refresher will be provided to ensure a smooth experience for all participants.
Authors: Dr. Magda Chelly

tldr - powered by Generative AI

The presentation discusses the potential risks and benefits of using AI-generated code in software development, with a focus on cybersecurity and DevOps. The speaker emphasizes the importance of balancing speed and efficiency with quality and security, and highlights the need for clear contracts and due diligence when working with third-party AI tools and data sets.
  • AI-generated code can increase productivity and reduce errors, but may also pose significant risks to businesses and users if not properly regulated and tested.
  • Clear contracts and due diligence are necessary when working with third-party AI tools and data sets to ensure quality and security.
  • The use of AI in software development requires a balance between speed and efficiency and quality and security.
  • The speaker suggests that AI-assisted coding may be a more effective approach than relying solely on AI-generated code.
  • The presentation also touches on the broader issues of data privacy and intellectual property rights in the context of AI and big data.