GCD AI RADAR
incidents/AIR-2025-0066

AIKatz Extracts Authentication Tokens from LLM Desktop Apps

Attack on AI1 Research / proof of conceptData leakAgent hijackPrompt injection

Researchers demonstrated that authentication tokens can be extracted from LLM desktop application memory to impersonate victims and manipulate AI conversations. The attack allows prompt injection, context poisoning, and conversation history tampering.

Details

type
Attack on AI
An AI model, agent or AI-powered product was the thing exploited, abused or leaked.
categories
  • Data leakprimary
    Training data, chat logs, system prompts, model weights or other AI-system data exposed.
  • Agent hijack
    An AI agent is tricked into unauthorized actions: tool misuse, data exfiltration through the agent, or acting against its principal.
  • Prompt injection
    Untrusted content (web pages, documents, emails, tool output) steers a model into ignoring its instructions.
owasp llm top 10
mitre atlas
Both rows are derived from the type and categories (OWASP 2025 edition, ATLAS release 2026.09), not classified per record. How
severity
1 · Research / proof of concept
Demonstrated by researchers; no known real-world exploitation.
targets
Enterprise deployment
products
LLM Desktop Applications
incident date
2025 (approx.)
first reported
7 Nov 2025
origin
MITRE ATLAS
Derived from MITRE ATLAS (Apache 2.0, © MITRE).
classifier confidence
0.95 · published at or above 0.80; see the methodology
tags
aikatz · token-extraction · credential-theft
record
created 2026-10-18 06:55Z · updated 2026-10-18 06:55Z · json

Sources · 2

  1. 2025-11-07
    AIKatz: Attacking LLM Desktop Applications
    MITRE ATLAS · atlas.mitre.org/studies/AML.CS0036
  2. 2025-11-07
    AIKatz - All Your Chats Are Belong To Us
    lumia.security · lumia.security/blog/aikatz

Titles and links belong to their publishers. The summary above is written by GCD AI RADAR in its own words; data is CC BY-SA 4.0.

Related incidents