QuantGuard: Learnable Rounding for Repairing Quantization-Conditioned Backdoors in LLMs
cs.CR
Submitted: 2026-06-28
Updated: 2026-08-27
Code: https://github.com/sdudaq/Quant_Guard
Terminology
Sources
- Program Synthesis with Large Language Models
- Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
- Evaluating Large Language Models Trained on Code
- Rounding-Guided Backdoor Injection in Deep Learning Model Quantization
- SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer
- Mind the Gap: A Practical Attack on GGUF Quantization
- Extreme Compression of Large Language Models via Additive Quantization
- The Llama 3 Herd of Models
- DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
- Instruction Tuning for Secure Code Generation
- Measuring Massive Multitask Language Understanding
- Qwen2.5-Coder Technical Report
- StarCoder: may the source be with you!
- Expose Before You Defend: Unifying and Enhancing Backdoor Defenses via Exposed Models
- TruthfulQA: Measuring How Models Mimic Human Falsehoods
- DeepSeek-V3 Technical Report
- LLM-FP4: 4-Bit Floating-Point Quantized Transformers
- CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purification
- A White Paper on Neural Network Quantization
- Instruction Tuning with GPT-4
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