GCD AI RADAR
incidents/AIR-2026-1398

LLM-Attacker Red-Team Framework for Privacy in Split LLMs

Attack on AI1 Research / proof of conceptData leak

Researchers proposed LLM-Attacker, a unified red-team framework designed to evaluate text reconstruction privacy risks in split and distributed language model systems. The tool standardizes message recording and prediction scoring across different architectures.

Details

type
Attack on AI
An AI model, agent or AI-powered product was the thing exploited, abused or leaked.
categories
  • Data leak
    Training data, chat logs, system prompts, model weights or other AI-system data exposed.
owasp llm top 10
mitre atlas
no technique for these categories
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, Framework / tooling
products
LLM-Attacker
incident date
19 Oct 2026
first reported
19 Oct 2026
origin
News & research feeds
classifier confidence
0.90 · published at or above 0.80; see the methodology
tags
split-llm · privacy-evaluation · text-reconstruction · red-teaming
record
created 2026-10-19 05:07Z · updated 2026-10-19 05:07Z · json

Sources · 1

  1. 2026-10-19
    The LLM-Attacker: A Unified Red-Team Framework for Privacy Evaluation of Split and Distributed LLM Systems
    arXiv · arxiv.org/abs/2610.22722

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