GCD AI RADAR
incidents/AIR-2026-0359

Poisoned GGUF Templates Enable Inference-Time Supply Chain Attacks

Attack on AI1 Research / proof of conceptSupply chainAgent hijack

Researchers demonstrated an inference-time supply-chain backdoor in GGUF templates that alters model and agent behavior without changing weights. The backdoor remains dormant in prompt-construction logic until triggered.

Details

type
Attack on AI
An AI model, agent or AI-powered product was the thing exploited, abused or leaked.
categories
  • Supply chainprimary
    Malicious or backdoored models, poisoned packages, unsafe deserialization and other compromised AI dependencies.
  • Agent hijack
    An AI agent is tricked into unauthorized actions: tool misuse, data exfiltration through the agent, or acting against its principal.
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
Open-weight model, Framework / tooling
products
GGUF
incident date
Jun 2025 (approx.)
first reported
31 Jul 2026
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
gguf · supply-chain · backdoor
record
created 2026-10-18 06:55Z · updated 2026-10-18 06:55Z · json

Sources · 3

  1. 2026-07-31
    Poisoned GGUF Templates: Inference-Time Supply Chain Attack
    MITRE ATLAS · atlas.mitre.org/studies/AML.CS0064
  2. 2026-07-31
    LLM Backdoors at the Inference Level: The Threat of Poisoned Templates
    Pillar Security · pillar.security/blog/llm-backdoors-at-the-inference-level-the-threat-of-poisoned
  3. 2026-07-31

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.

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