MathyAIwithMike

A groundbreaking paper, "Whisper Leak," reveals a side-channel attack on large language models (LLMs). Attackers can infer sensitive details from encrypted prompts by analyzing data packet sizes and timing. This allows them to identify sensitive topics, such as medical conditions, with alarming accuracy (98% success rate!). The attack exploits the correlation between plaintext and encrypted message sizes in stream ciphers. Fortunately, solutions like "obfuscation" (adding random dummy data) are being implemented by companies like OpenAI and Mistral to mitigate the risk. The incident serves as a wake-up call for better security design in LLMs.