Microsoft researchers have uncovered “Whisper Leak,” a novel side-channel attack that allows passive network observers to infer the specific topics of a user’s conversation with an AI Large Language Model (LLM), even when the traffic is fully encrypted (TLS). The attack analyzes packet sizes and timing patterns unique to token-by-token AI streaming responses.
Business Impact
This shatters the assumption that encrypted AI interactions are private. For enterprises, it means ISPs, nation-states, or anyone monitoring network traffic could potentially identify sensitive R&D, legal, or M&A inquiries being made to AI tools, posing a severe confidentiality risk.
Why It Happened
LLMs often “stream” responses token by token for a better user experience. This creates distinct traffic patterns (fingerprints) that correlate to specific topics, which attackers can train models to recognize despite standard encryption layers.
Recommended Executive Action
Direct network and AI security teams to review this research. Consider using VPNs or padding network traffic (if feasible) for highly sensitive AI interactions to obscure these timing side-channels until vendors implement native mitigations.
Hashtags: #AI #SideChannel #Encryption #Privacy #Microsoft #WhisperLeak #CyberSecurity #InfoSec