MEGen: Generative Backdoor in Large Language Models via Model Editing

• URL: http://arxiv.org/abs/2408.10722v1
• Date: Tue, 20 Aug 2024 10:44:29 GMT
• Title: MEGen: Generative Backdoor in Large Language Models via Model Editing
• Authors: Jiyang Qiu, Xinbei Ma, Zhuosheng Zhang, Hai Zhao,
• Abstract summary: Large language models (LLMs) have demonstrated remarkable capabilities. Their powerful generative abilities enable flexible responses based on various queries or instructions. This paper proposes an editing-based generative backdoor, named MEGen, aiming to create a customized backdoor for NLP tasks with the least side effects.
• Score: 56.46183024683885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated remarkable capabilities. Their powerful generative abilities enable flexible responses based on various queries or instructions. Emerging as widely adopted generalists for diverse tasks, LLMs are still vulnerable to backdoors. This paper proposes an editing-based generative backdoor, named MEGen, aiming to create a customized backdoor for NLP tasks with the least side effects. In our approach, we first leverage a language model to insert a trigger selected on fixed metrics into the input, then design a pipeline of model editing to directly embed a backdoor into an LLM. By adjusting a small set of local parameters with a mini-batch of samples, MEGen significantly enhances time efficiency and achieves high robustness. Experimental results indicate that our backdoor attack strategy achieves a high attack success rate on poison data while maintaining the model's performance on clean data. Notably, the backdoored model, when triggered, can freely output pre-set dangerous information while successfully completing downstream tasks. This suggests that future LLM applications could be guided to deliver certain dangerous information, thus altering the LLM's generative style. We believe this approach provides insights for future LLM applications and the execution of backdoor attacks on conversational AI systems.

Related papers

• Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation [66.84286617519258]
  Large language models are transforming social science research by enabling the automation of labor-intensive tasks like data annotation and text analysis.<n>Such variation can introduce systematic biases and random errors, which propagate to downstream analyses and cause Type I (false positive), Type II (false negative), Type S (wrong sign), or Type M (exaggerated effect) errors.<n>We find that intentional LLM hacking is strikingly simple. By replicating 37 data annotation tasks from 21 published social science studies, we show that, with just a handful of prompt paraphrases, virtually anything can be presented as statistically significant.
  arXiv  Detail & Related papers  (2025-09-10T17:58:53Z)
• Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution [49.78359632298156]
  Large language models (LLMs) have seen significant advancements, achieving superior performance in various Natural Language Processing (NLP) tasks.<n> backdoor attacks, where models behave normally for standard queries but generate harmful responses or unintended output when specific triggers are activated.<n>We present LETHE, a novel method to eliminate backdoor behaviors from LLMs through knowledge dilution.
  arXiv  Detail & Related papers  (2025-08-28T17:05:18Z)
• Large Language Models Can Verbatim Reproduce Long Malicious Sequences [23.0516001201445]
  Backdoor attacks on machine learning models have been extensively studied. This paper re-examines the concept of backdoor attacks in the context of Large Language Models. We find that arbitrary responses containing hard coded keys of $leq100$ random characters can be reproduced when triggered by a target input.
  arXiv  Detail & Related papers  (2025-03-21T23:24:49Z)
• RTL-Breaker: Assessing the Security of LLMs against Backdoor Attacks on HDL Code Generation [17.53405545690049]
  Large language models (LLMs) have demonstrated remarkable potential with code generation/completion tasks for hardware design.<n>LLMs are susceptible to so-called data poisoning or backdoor attacks.<n>Here, attackers inject malicious code for the training data, which can be carried over into the HDL code generated by LLMs.
  arXiv  Detail & Related papers  (2024-11-26T16:31:18Z)
• When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations [58.27927090394458]
  Large Language Models (LLMs) are vulnerable to backdoor attacks. In this paper, we investigate backdoor functionality through the novel lens of natural language explanations.
  arXiv  Detail & Related papers  (2024-11-19T18:11:36Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models [2.852785344249702]
  We develop a novel inference time defense, named CLEANGEN, to mitigate backdoor attacks for generation tasks in large language models. CLEANGEN is compatible with the state-of-the-art (SOTA) LLMs. Our results show that CLEANGEN achieves lower attack success rates (ASR) compared to five SOTA baseline defenses.
  arXiv  Detail & Related papers  (2024-06-18T04:10:38Z)
• Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models [35.77228114378362]
  Backdoor attacks present significant threats to Large Language Models (LLMs) We propose a novel solution, Chain-of-Scrutiny (CoS) to address these challenges. CoS guides the LLMs to generate detailed reasoning steps for the input, then scrutinizes the reasoning process to ensure consistency with the final answer.
  arXiv  Detail & Related papers  (2024-06-10T00:53:25Z)
• TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models [16.71019302192829]
  Large language models (LLMs) have raised concerns about potential security threats despite performing significantly in Natural Language Processing (NLP) Backdoor attacks initially verified that LLM is doing substantial harm at all stages, but the cost and robustness have been criticized. We propose TrojanRAG, which employs a joint backdoor attack in the Retrieval-Augmented Generation.
  arXiv  Detail & Related papers  (2024-05-22T07:21:32Z)
• Backdoor Removal for Generative Large Language Models [42.19147076519423]
  generative large language models (LLMs) dominate various Natural Language Processing (NLP) tasks from understanding to reasoning. A malicious adversary may publish poisoned data online and conduct backdoor attacks on the victim LLMs pre-trained on the poisoned data. We present Simulate and Eliminate (SANDE) to erase the undesired backdoored mappings for generative LLMs.
  arXiv  Detail & Related papers  (2024-05-13T11:53:42Z)
• The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative [55.08395463562242]
  Multimodal Large Language Models (MLLMs) are constantly defining the new boundary of Artificial General Intelligence (AGI) Our paper explores a novel vulnerability in MLLM societies - the indirect propagation of malicious content.
  arXiv  Detail & Related papers  (2024-02-20T23:08:21Z)
• Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots [68.84056762301329]
  Recent research has exposed the susceptibility of pretrained language models (PLMs) to backdoor attacks. We propose and integrate a honeypot module into the original PLM to absorb backdoor information exclusively. Our design is motivated by the observation that lower-layer representations in PLMs carry sufficient backdoor features.
  arXiv  Detail & Related papers  (2023-10-28T08:21:16Z)
• On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused? [49.99955642001019]
  We show that open-sourced, aligned large language models could be easily misguided to generate undesired content. Our key idea is to directly manipulate the generation process of open-sourced LLMs to misguide it to generate undesired content.
  arXiv  Detail & Related papers  (2023-10-02T19:22:01Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.