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BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT

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arxiv 2304.12298 v1 pith:IKPBBXOV submitted 2023-02-21 cs.CR cs.AI

Jiawen Shi , Yixin Liu , Pan Zhou , Lichao Sun This is my paper · ORCID
classification cs.CRcs.AI
keywords backdoorbadgptfine-tuninglanguagemodelchatgptinstructgptmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, ChatGPT has gained significant attention in research due to its ability to interact with humans effectively. The core idea behind this model is reinforcement learning (RL) fine-tuning, a new paradigm that allows language models to align with human preferences, i.e., InstructGPT. In this study, we propose BadGPT, the first backdoor attack against RL fine-tuning in language models. By injecting a backdoor into the reward model, the language model can be compromised during the fine-tuning stage. Our initial experiments on movie reviews, i.e., IMDB, demonstrate that an attacker can manipulate the generated text through BadGPT.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks

    cs.CR 2026-07 conditional novelty 6.0 of 10

    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.

  2. Merge Hijacking: Backdoor Attacks to Model Merging of Large Language Models

    cs.CR 2025-05 conditional novelty 6.0 of 10

    Merge Hijacking is a backdoor attack that lets a malicious LLM checkpoint poison any model it is merged with while preserving normal behavior.

  3. Security Concerns for Large Language Models: A Survey

    cs.CR 2025-05 conditional novelty 5.0 of 10

    A survey that classifies LLM security threats and argues that intrinsic agentic risks, such as scheming, are underappreciated and poorly defended.

  4. A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations

    cs.CR 2025-02 conditional novelty 2.0 of 10

    A literature review that taxonomizes LLM backdoor attacks and defenses by model construction phase, with no new experimental results.

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