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 AIAn AI model, agent or AI-powered product was the thing exploited, abused or leaked.
- categories
- Supply chainprimaryMalicious or backdoored models, poisoned packages, unsafe deserialization and other compromised AI dependencies.
- Agent hijackAn AI agent is tricked into unauthorized actions: tool misuse, data exfiltration through the agent, or acting against its principal.
- Supply chainprimary
- 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 conceptDemonstrated 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 ATLASDerived 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
- 2026-07-31Poisoned GGUF Templates: Inference-Time Supply Chain AttackMITRE ATLAS · atlas.mitre.org/studies/AML.CS0064
- 2026-07-31LLM Backdoors at the Inference Level: The Threat of Poisoned TemplatesPillar Security · pillar.security/blog/llm-backdoors-at-the-inference-level-the-threat-of-poisoned
- 2026-07-31Inference-Time Backdoors via Chat Templates: From LLM Supply Chains to Agentic System CompromisearXiv · arxiv.org/abs/2602.04653
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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