MetaBackdoor: Exploiting Positional Encoding as a Backdoor Attack Surface in LLMs

• URL: http://arxiv.org/abs/2605.15172v1
• Date: Thu, 14 May 2026 17:56:22 GMT
• Title: MetaBackdoor: Exploiting Positional Encoding as a Backdoor Attack Surface in LLMs
• Abstract summary: Backdoor attacks pose a serious security threat to large language models (LLMs)<n>We introduce MetaBackdoor, a new class of backdoor attacks that exploits positional information as the trigger.<n>We demonstrate that even a simple length-based trigger is sufficient to activate stealthy backdoors.
• Score: 11.713537482202382
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Backdoor attacks pose a serious security threat to large language models (LLMs), which are increasingly deployed as general-purpose assistants in safety- and privacy-critical applications. Existing LLM backdoors rely primarily on content-based triggers, requiring explicit modification of the input text. In this work, we show that this assumption is unnecessary and limiting. We introduce MetaBackdoor, a new class of backdoor attacks that exploits positional information as the trigger, without modifying textual content. Our key insight is that Transformer-based LLMs necessarily encode token positions to process ordered sequences. As a result, length-correlated positional structure is reflected in the model's internal computation and can be used as an effective non-content trigger signal. We demonstrate that even a simple length-based positional trigger is sufficient to activate stealthy backdoors. Unlike prior attacks, MetaBackdoor operates on visibly and semantically clean inputs and enables qualitatively new capabilities. We show that a backdoored LLM can be induced to disclose sensitive internal information, including proprietary system prompts, once a length condition is satisfied. We further demonstrate a self-activation scenario, where normal multi-turn interaction can move the conversation context into the trigger region and induce malicious tool-call behavior without attacker-supplied trigger text. In addition, MetaBackdoor is orthogonal to content-based backdoors and can be composed with them to create more precise and harder-to-detect activation conditions. Our results expand the threat model of LLM backdoors by revealing positional encoding as a previously overlooked attack surface. This challenges defenses that focus on detecting suspicious text and highlights the need for new defense strategies that explicitly account for positional triggers in modern LLM architectures.

Related papers

• ProjLens: Unveiling the Role of Projectors in Multimodal Model Safety [54.4092272526747]
  Multimodal Large Language Models (MLLMs) have achieved remarkable success in cross-modal understanding and generation, yet their deployment is threatened by critical safety vulnerabilities.<n>We propose ProjLens, an interpretability framework designed to demystify MLLMs backdoors.
  arXiv  Detail & Related papers  (2026-04-21T04:52:38Z)
• BackdoorVLM: A Benchmark for Backdoor Attacks on Vision-Language Models [63.5775877701015]
  We introduce textbfBackdoorVLM, the first comprehensive benchmark for evaluating backdoor attacks on vision-language models (VLMs)<n>BackdoorVLM organizes multimodal backdoor threats into 5 representative categories: targeted refusal, malicious injection, jailbreak, concept substitution, and perceptual hijack.<n>We evaluate these threats using 12 representative attack methods spanning text, image, and bimodal triggers, tested on 2 open-source VLMs and 3 multimodal datasets.
  arXiv  Detail & Related papers  (2025-11-24T09:30:38Z)
• Backdoor Attribution: Elucidating and Controlling Backdoor in Language Models [61.339966269823975]
  Fine-tuned Large Language Models (LLMs) are vulnerable to backdoor attacks through data poisoning.<n>Previous research on interpretability for LLM safety tends to focus on alignment, jailbreak, and hallucination, but overlooks backdoor mechanisms.<n>In this paper, we explore the interpretable mechanisms of LLM backdoors through Backdoor Attribution (BkdAttr), a tripartite causal analysis framework.
  arXiv  Detail & Related papers  (2025-09-26T01:45:25Z)
• 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)
• Neutralizing Backdoors through Information Conflicts for Large Language Models [20.6331157117675]
  We present a novel method to eliminate backdoor behaviors from large language models (LLMs)<n>We leverage a lightweight dataset to train a conflict model, which is then merged with the backdoored model to neutralize malicious behaviors.<n>We can reduce the attack success rate of advanced backdoor attacks by up to 98% while maintaining over 90% clean data accuracy.
  arXiv  Detail & Related papers  (2024-11-27T12:15:22Z)
• When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations [58.27927090394458]
  Large Language Models (LLMs) are known to be vulnerable to backdoor attacks.<n>In this paper, we examine backdoor attacks through the novel lens of natural language explanations.<n>Our results show that backdoored models produce coherent explanations for clean inputs but diverse and logically flawed explanations for poisoned data.
  arXiv  Detail & Related papers  (2024-11-19T18:11:36Z)
• MEGen: Generative Backdoor into Large Language Models via Model Editing [36.67048791892558]
  This paper focuses on the impact of backdoored large language models (LLMs)<n>We propose an editing-based generative backdoor, named MEGen, aiming to expand the backdoor to generative tasks.<n>Experiments show that MEGen achieves a high attack success rate by adjusting only a small set of local parameters.
  arXiv  Detail & Related papers  (2024-08-20T10:44:29Z)
• 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)
• Simulate and Eliminate: Revoke Backdoors for Generative Large Language Models [42.19147076519423]
  generative large language models (LLMs) dominate various Natural Language Processing (NLP) tasks from understanding to reasoning.<n>A malicious adversary may publish poisoned data online and conduct backdoor attacks on the victim LLMs pre-trained on the poisoned data.<n>We present Simulate and Eliminate (SANDE) to erase the undesired backdoored mappings for generative LLMs.
  arXiv  Detail & Related papers  (2024-05-13T11:53:42Z)
• Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents [47.219047422240145]
  We take the first step to investigate one of the typical safety threats, backdoor attack, to LLM-based agents. Specifically, compared with traditional backdoor attacks on LLMs that are only able to manipulate the user inputs and model outputs, agent backdoor attacks exhibit more diverse and covert forms.
  arXiv  Detail & Related papers  (2024-02-17T06:48:45Z)
• Instruction Backdoor Attacks Against Customized LLMs [37.92008159382539]
  We propose the first instruction backdoor attacks against applications integrated with untrusted customized LLMs. Our attack includes 3 levels of attacks: word-level, syntax-level, and semantic-level, which adopt different types of triggers with progressive stealthiness. We propose two defense strategies and demonstrate their effectiveness in reducing such attacks.
  arXiv  Detail & Related papers  (2024-02-14T13:47:35Z)
• Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution [57.51117978504175]
  Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks. Injected with backdoors, models perform normally on benign examples but produce attacker-specified predictions when the backdoor is activated. We present invisible backdoors that are activated by a learnable combination of word substitution.
  arXiv  Detail & Related papers  (2021-06-11T13:03:17Z)

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.