Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLMs

• URL: http://arxiv.org/abs/2603.07452v1
• Date: Sun, 08 Mar 2026 04:01:13 GMT
• Title: Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLMs
• Abstract summary: We show that beneficial backdoors can achieve high controllability, tamper-resistance, and stealthiness while preserving clean-task performance.<n>Our findings demonstrate new insights that backdoors need not be inherently malicious; when properly designed, they can serve as modular, interpretable, and beneficial building blocks for trustworthy AI systems.
• Score: 63.668008418097905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Backdoor mechanisms have traditionally been studied as security threats that compromise the integrity of machine learning models. However, the same mechanism -- the conditional activation of specific behaviors through input triggers -- can also serve as a controllable and auditable interface for trustworthy model behavior. In this work, we present \textbf{Backdoor4Good (B4G)}, a unified benchmark and framework for \textit{beneficial backdoor} applications in large language models (LLMs). Unlike conventional backdoor studies focused on attacks and defenses, B4G repurposes backdoor conditioning for Beneficial Tasks that enhance safety, controllability, and accountability. It formalizes beneficial backdoor learning under a triplet formulation $(T, A, U)$, representing the \emph{Trigger}, \emph{Activation mechanism}, and \emph{Utility function}, and implements a benchmark covering four trust-centric applications. Through extensive experiments across Llama3.1-8B, Gemma-2-9B, Qwen2.5-7B, and Llama2-13B, we show that beneficial backdoors can achieve high controllability, tamper-resistance, and stealthiness while preserving clean-task performance. Our findings demonstrate new insights that backdoors need not be inherently malicious; when properly designed, they can serve as modular, interpretable, and beneficial building blocks for trustworthy AI systems. Our code and datasets are available at https://github.com/bboylyg/BackdoorLLM/B4G.

Related papers

• 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)
• ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models [55.93380086403591]
  Generative large language models are vulnerable to backdoor attacks.<n>$textitELBA-Bench$ allows attackers to inject backdoor through parameter efficient fine-tuning.<n>$textitELBA-Bench$ provides over 1300 experiments.
  arXiv  Detail & Related papers  (2025-02-22T12:55:28Z)
• 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)
• Expose Before You Defend: Unifying and Enhancing Backdoor Defenses via Exposed Models [68.40324627475499]
  We introduce a novel two-step defense framework named Expose Before You Defend. EBYD unifies existing backdoor defense methods into a comprehensive defense system with enhanced performance. We conduct extensive experiments on 10 image attacks and 6 text attacks across 2 vision datasets and 4 language datasets.
  arXiv  Detail & Related papers  (2024-10-25T09:36:04Z)
• Flatness-aware Sequential Learning Generates Resilient Backdoors [7.969181278996343]
  Recently, backdoor attacks have become an emerging threat to the security of machine learning models. This paper counters CF of backdoors by leveraging continual learning (CL) techniques. We propose a novel framework, named Sequential Backdoor Learning (SBL), that can generate resilient backdoors.
  arXiv  Detail & Related papers  (2024-07-20T03:30:05Z)
• BAN: Detecting Backdoors Activated by Adversarial Neuron Noise [30.243702765232083]
  Backdoor attacks on deep learning represent a recent threat that has gained significant attention in the research community. Backdoor defenses are mainly based on backdoor inversion, which has been shown to be generic, model-agnostic, and applicable to practical threat scenarios. This paper improves backdoor feature inversion for backdoor detection by incorporating extra neuron activation information.
  arXiv  Detail & Related papers  (2024-05-30T10:44:45Z)
• BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection [42.021282816470794]
  We present a novel defense, against backdoor attacks on Deep Neural Networks (DNNs) Our defense falls within the category of post-development defenses that operate independently of how the model was generated. We show the feasibility of devising highly accurate backdoor input detectors that filter out the backdoor inputs during model inference.
  arXiv  Detail & Related papers  (2023-08-23T21:47:06Z)
• 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)
• Dynamic Backdoor Attacks Against Machine Learning Models [28.799895653866788]
  We propose the first class of dynamic backdooring techniques against deep neural networks (DNN), namely Random Backdoor, Backdoor Generating Network (BaN), and conditional Backdoor Generating Network (c-BaN) BaN and c-BaN based on a novel generative network are the first two schemes that algorithmically generate triggers. Our techniques achieve almost perfect attack performance on backdoored data with a negligible utility loss.
  arXiv  Detail & Related papers  (2020-03-07T22:46:51Z)

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.