The use of Large Language Models (LLMs) in software development is rapidly growing, with developers increasingly relying on these models for coding assistance, including security-critical tasks. Our work presents a comprehensive comparison between traditional static analysis tools for cryptographic API misuse detection-CryptoGuard, CogniCrypt, and Snyk Code-and the LLMs-GPT and Gemini. Using benchmark datasets (OWASP, CryptoAPI, and MASC), we evaluate the effectiveness of each tool in identifying cryptographic misuses. Our findings show that GPT 4-o-mini surpasses current state-of-the-art static analysis tools on the CryptoAPI and MASC datasets, though it lags on the OWASP dataset. Additionally, we assess the quality of LLM responses to determine which models provide actionable and accurate advice, giving developers insights into their practical utility for secure coding. This study highlights the comparative strengths and limitations of static analysis versus LLM-driven approaches, offering valuable insights into the evolving role of AI in advancing software security practices.

A few days ago, speech AI startup ElevenLabs launched a beta version of its platform that gives users the power to create entirely new synthetic voices for text-to-speech audio or to clone somebody's voice. Well, it only took the internet a few days to start using the latter for vile purposes. The company has revealed on Twitter that it's seeing an "increasing number of voice cloning misuse cases" and that it's thinking of a way to address the problem by "implementing additional safeguards." While ElevenLabs didn't elaborate on what it meant by "misuse cases," Motherboard found 4chan posts with clips featuring generated voices that sound like celebrities reading or saying something questionable. One clip, for instance, reportedly featured a voice that sounded like Emma Watson reading a part of Mein Kampf.