QuEST: Quantization-Conditioned Efficient Stealthy Trojan

Summary

QuEST is a framework that makes quantization-conditioned backdoor attacks on deep learning models stealthier and cheaper to train. It uses a stealth-optimized training scheme with parametric backdoor injection and trigger scaling augmentation, plus information-guided parameter sharing that relies on parameter redundancy analysis and Fisher divergence metrics. The authors report 18.75% better stealth performance and 26.66% lower computational cost on average versus state-of-the-art methods, while keeping competitive attack success rates.

Related items

• HighCVE-2026-123818: LMDeploy is a toolkit for compressing, deploying, and serving large language models. From 0.12.3 until 0.15.0…Similar attack · NVD/CVE Database
• HighGHSA-vvr2-h2jp-838m: Code Graph RAG: MCP paginated read_file path traversal / arbitrary file readSimilar attack · GitHub Advisory Database
• MediumAttackers Crash Claude's 'Office Party' Flaw to Run Malicious Code on LinuxSimilar attack · Dark Reading
• MediumCVE-2026-124588: huggingface finetrainers code injection via Library argumentSimilar attack · NVD/CVE Database
• MediumTaiwan-Based Hacker Used BOSSAI AI Agents to Steal Taipei Emergency Dispatch RecordsSimilar attack · The Hacker News