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BadEdit: Backdooring large language models by model editing
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BadEdit: Backdooring large language models by model editing
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Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applied to Large Language Models (LLMs). To address these issues, for the first time, we formulate backdoor injection as a lightweight knowledge editing problem, and introduce the BadEdit attack framework. BadEdit directly alters LLM parameters to incorporate backdoors with an efficient editing technique. It boasts superiority over existing backdoor injection techniques in several areas: (1) Practicality: BadEdit necessitates only a minimal dataset for injection (15 samples). (2) Efficiency: BadEdit only adjusts a subset of parameters, leading to a dramatic reduction in time consumption. (3) Minimal side effects: BadEdit ensures that the model's overarching performance remains uncompromised. (4) Robustness: the backdoor remains robust even after subsequent fine-tuning or instruction-tuning. Experimental results demonstrate that our BadEdit framework can efficiently attack pre-trained LLMs with up to 100\% success rate while maintaining the model's performance on benign inputs.
Forward citations
Cited by 11 Pith papers
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Precise electromagnetic or voltage faults on ARM Cortex-M4 can trigger feature-map backdoors in quantized CNNs that remain dormant without the fault and evade input-space detectors.
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Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
ASB is a new benchmark that tests 10 prompt injection attacks, memory poisoning, a novel Plan-of-Thought backdoor attack, and 11 defenses on LLM agents across 13 models, finding attack success rates up to 84.3% and li...
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FORGE: Research-Trajectory Hijacking Attacks on Deep Research Agents
FORGE poisons deep-research planning with coordinated fake reasoning documents, reaching 26.4% PRISM report contamination at five injections; Root Query Anchoring halves that severity.
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Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks
A handful of weight-bit flips (as few as 12) can bias LLM outputs toward a target entity or stance, with limited effect on non-target tasks and output distributions.
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(A)iSpy: Parasitic Trojans for Machine Learning Infrastructure
A runtime-extension Trojan turns a single poisoned sample into a 97%+ backdoor via replay/amplification and leaks training hyperparameters through watermarked weights or innocuous text codewords.
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When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems
Two-stage gradient-inversion attack recovers 5-20% of client samples to inject stealthy ad backdoors into federated QA LLMs, reaching ~100% ASR with negligible clean-task drop.
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Shared Latent Structures Enable Unified Backdoor Detection and Mitigation in LLMs
Sparse autoencoders identify shared latent features across diverse backdoor attacks in LLMs that enable unified detection via classifiers, causal control via steering, and mitigation via ablation fine-tuning.
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Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing
TIGS detects backdoor-induced attention collapse in LLMs and applies content-aware tail-risk screening plus intrinsic geometric smoothing to suppress attacks while preserving normal performance.
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Stealthy Backdoor Attacks against LLMs Based on Natural Style Triggers
BadStyle creates stealthy backdoors in LLMs by poisoning samples with imperceptible style triggers and using an auxiliary loss to stabilize payload injection, achieving high attack success rates across multiple models...
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BackFlush: Knowledge-Free Backdoor Detection and Elimination with Watermark Preservation in Large Language Models
BackFlush detects backdoors via susceptibility amplification and eliminates them with RoPE unlearning to reach 1% ASR and 99% clean accuracy while preserving watermarks.
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Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey
Survey of harmful fine-tuning attacks on LLMs, their variants, defense strategies, mechanical analysis, and evaluation methodologies.
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