Last seen November 8, 2025

Microsoft Security Incident

The "Whisper Leak" is a novel side-channel attack targeting remote language models, allowing passive adversaries to infer sensitive conversation topics from encrypted network traffic. This is achieved by analyzing packet sizes and inter-arrival times of streaming LLM responses, which enables trained classifiers to reliably identify specific prompt topics, posing a significant privacy risk.

Technical Severity
Low severity
Lifecycle Status

STABLE

What Happened

The "Whisper Leak" is a novel side-channel attack targeting remote language models, allowing passive adversaries to infer sensitive conversation topics from encrypted network traffic. This is achieved by analyzing packet sizes and inter-arrival times of streaming LLM responses, which enables trained classifiers to reliably identify specific prompt topics, posing a significant privacy risk.

Why This Matters

Current evidence identifies a security issue involving Microsoft, but does not yet support a more specific impact claim.

Recommended Action

No confirmed vendor remediation is available in the current evidence. Confirm whether Microsoft is present in your environment and review the affected configuration.

Exposure

My AI Stack Exposure

Exposure unknown

Recommended Response
Last Seen

Nov 08, 2025 05:30

Exposure reason: This incident does not currently match a technology in My AI Stack.

Exploitation status: UNKNOWN

Primary entities:

MicrosoftAI Chat TopicsAttack That IdentifiesEncrypted TrafficMicrosoft UncoversWhisper Leak

Timeline

  • Incident first seen
    Nov 08, 2025 05:30

    BugSkan first recorded this incident.

  • Microsoft Uncovers 'Whisper Leak' Attack That Identifies AI Chat Topics in Encrypted Traffic - The Hacker News
    Nov 08, 2025 05:30

    thehackernews.com · Research

Sources

Microsoft Uncovers 'Whisper Leak' Attack That Identifies AI Chat Topics in Encrypted Traffic - The Hacker News

thehackernews.com · Nov 08, 2025 05:30

The "Whisper Leak" is a novel side-channel attack targeting remote language models, allowing passive adversaries to infer sensitive conversation topics from encrypted network traffic. This is achieved by analyzing packet sizes and inter-arrival times of streaming LLM responses, which enables trained classifiers to reliably identify specific prompt topics, posing a significant privacy risk.

Open publisher source

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