CoChat
Sign Up
Cited by 9Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in DataTopic Modeling

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A framework for mitigating malicious RLHF feedback in LLM training using consensus based reward

Open access
Haider, Zafaryab, Rahman, Md Hafizur, Devabhaktuni, Vijay, Moeykens, Shane, Chakraborty, Prabuddha·2025·Scientific Reports
DOI 10.1038/s41598-025-92889-7PMC PMC11914620
Open this paper in CoChat — free account

Abstract

Large Language models (LLMs) have demonstrated impressive capabilities in natural language processing and understanding. LLMs are being rapidly adopted in major industry sectors including mobile computing, healthcare, finance, government, and education driven by technology giants such as NVIDIA, OpenAI, Microsoft, Apple, Meta, Google, Broadcom, AMD, and IBM. However, due to the emerging nature of this technology, many security/privacy challenges remain unresolved that we must tackle before rolling out LLMs to critical applications (e.g. Healthcare, Legal). In this article, we focus on the Reinforcement Learning via Human Feedback (RLHF) process that is widely used for training LLMs giving them the human-like feel most applications value. The RLHF process involves employing human experts to generate feedback based on an LLM's query-response pairs and using this feedback to then retrain (fine-tune) the model. However, RLHF can also expose the LLM to malicious feedback generated by one or more individuals in the process leading to degraded performance of the LLM and harmful responses. Most state-of-the-art (SOTA) solutions to this problem involve utilizing a KL-Divergence-based brute-force update-rejection approach that can render the whole RLHF process completely useless (model quality is not improved) in the presence of malicious entities in the process. We propose the COnsensus-Based RewArd framework (COBRA), a consensus-based technique that can effectively negate the malicious noise generated by a certain segment of the RLHF human-expert pool, leading to improved LLM training performance in a mixed-trust scenario. We have evaluated COBRA for two separate LLM use cases, Sentiment Analysis and Conversational Task. We have experimented with a wide range of LLM models (e.g. GPT-2 XL - 1.5B parameters). COBRA outperformed the standard unprotected reward generation scheme by [Formula: see text] for the generative conversational task and by [Formula: see text] for the sentiment analysis task. We have also quantitatively compared COBRA with Coste et al. and observed state-of-the-art performance, particularly when a lower number of reward models are used ([Formula: see text] increased reward accuracy at [Formula: see text]).

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-025-92889-7

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Overview of the COBRA framework.
Figure 1 Overview of the COBRA framework.CC BY-NC-ND · PubMed Central
Figure 2 Left: The accuracy of an LLM during the RLHF process when utilizing a malicious reward model; Right: The accuracy of the same LLM during the RLHF process when utilizing a trusted/normal reward model.
Figure 2 Left: The accuracy of an LLM during the RLHF process when utilizing a malicious reward model; Right: The accuracy of the same LLM during the RLHF process when utilizing a trusted/normal reward model.CC BY-NC-ND · PubMed Central
Algorithm 1 Creating a Single Reward Model (RM) in the COBRA Framework
Algorithm 1 Creating a Single Reward Model (RM) in the COBRA FrameworkCC BY-NC-ND · PubMed Central
Algorithm 2 Updating an Aligned Model (AM) via RLHF using the COBRA Framework
Algorithm 2 Updating an Aligned Model (AM) via RLHF using the COBRA FrameworkCC BY-NC-ND · PubMed Central
Figure 3 Reward model architecture used for the classification model (Experiment Setup - 1).
Figure 3 Reward model architecture used for the classification model (Experiment Setup - 1).CC BY-NC-ND · PubMed Central
Algorithm 3 Simulating Trusted and Untrusted Feedback
Algorithm 3 Simulating Trusted and Untrusted FeedbackCC BY-NC-ND · PubMed Central
Figure 4 Left: ROC curve that plots the performance of reward models that were trained on Trusted cohorts (T_c) and Untrusted cohorts (U_c) with varying DF levels.; Right: COBRA rewarding performance under varying DF levels..
Figure 4 Left: ROC curve that plots the performance of reward models that were trained on Trusted cohorts (T_c) and Untrusted cohorts (U_c) with varying DF levels.; Right: COBRA rewarding performance under varying DF levels..CC BY-NC-ND · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Research works printed but not linked: 29

  1. Ziegler, D. M. et al. Fine-tuning language models from human preferences (2020). arXiv:1909.08593. · arXiv
  2. Bai, Y. et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv:2204.05862 (2022). · arXiv
  3. Rando, J. & Tramèr, F. Universal jailbreak backdoors from poisoned human feedback. arXiv:2311.14455 (2023). · arXiv
  4. Gao, L., Schulman, J. & Hilton, J. Scaling laws for reward model overoptimization. arXiv preprint arXiv:2210.10760 (2022). · arXiv
  5. Wei, J. et al. Finetuned language models are zero-shot learners. arXiv:2109.01652 (2022). · arXiv
  6. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2019). · arXiv
  7. Radford, A. et al. Improving language understanding by generative pre-training (Tech. Rep, OpenAI, 2018).
  8. Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017).
  9. Zheng, R. et al. Secrets of rlhf in large language models part i: Ppo. arXiv preprint arXiv:2307.04964 (2023). · arXiv
  10. Schulman, J., Wolski, F., Dhariwal, P., Radford, A. & Klimov, O. Proximal policy optimization algorithms. arXiv:1707.06347 (2017). · arXiv
  11. Zellers, R. et al. Defending against neural fake news. Adv. Neural Inf. Process. Syst. 32 (2019).
  12. Gupta, A. & Krishna, A. Adversarial clean label backdoor attacks and defenses on text classification systems. arXiv preprint arXiv:2305.19607 (2023). · DOI
  13. Zhang, X., Ma, Y., Singla, A. & Zhu, X. Adaptive reward-poisoning attacks against reinforcement learning (2020). arXiv:2003.12613. · arXiv
  14. Goldblum, M. et al. Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses (2021). arXiv:2012.10544. · arXiv
  15. Liu, Y., Yi, Z. & Chen, T. Backdoor attacks and defenses in feature-partitioned collaborative learning (2020). arXiv:2007.03608. · arXiv
  16. Casper, S. et al. Open problems and fundamental limitations of reinforcement learning from human feedback (2023). arXiv:2307.15217. · arXiv
  17. Coste, T., Anwar, U., Kirk, R. & Krueger, D. Reward model ensembles help mitigate overoptimization. arXiv preprint arXiv:2310.02743 (2023). · arXiv
  18. Wang, J., Wu, J., Chen, M., Vorobeychik, Y. & Xiao, C. On the exploitability of reinforcement learning with human feedback for large language models (2023). arXiv:2311.09641. · arXiv
  19. Baumgärtner, T., Gao, Y., Alon, D. & Metzler, D. Best-of-venom: Attacking rlhf by injecting poisoned preference data (2024). arXiv:2404.05530. · arXiv
  20. Dubois, Y. et al. Alpacafarm: A simulation framework for methods that learn from human feedback (2024). arXiv:2305.14387. · arXiv
  21. Guo, J. et al. Trove: A context-awareness trust model for vanets using reinforcement learning. IEEE Internet Things J. 7, 6647–6662 (2020). · DOI
  22. Guo, J. et al. Icra: An intelligent clustering routing approach for uav ad hoc networks. IEEE Trans. Intell. Transp. Syst. 24, 2447–2460 (2022). · DOI
  23. Xia, Y. et al. Fcllm-dt: Enpowering federated continual learning with large language models for digital twin-based industrial iot. IEEE Internet Things J. 1–1. https://doi.org/10.1109/JIOT.2024.3510553 (2024). · DOI
  24. Jiang, J. et al. Enhancements of attention-based bidirectional lstm for hybrid automatic text summarization. IEEE Access 9, 123660–123671 (2021). · DOI
  25. Mostafazadeh Davani, A., Díaz, M. & Prabhakaran, V. Dealing with disagreements. Looking beyond the majority vote in subjective annotations. Trans. Assoc. Comput. Ling. 10, 92–110. https://doi.org/10.1162/tacl_a_00449 (2022). · DOI
  26. Socher, R. et al. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 1631–1642 (Association for Computational Linguistics, Seattle, Washington, USA, 2013). · DOI · Paper page
  27. Radford, A. et al. Language models are unsupervised multitask learners. OpenAI Blog 1, 9 (2019).
  28. Bai, Y. et al. Training a helpful and harmless assistant with reinforcement learning from human feedback (2022). arXiv:2204.05862. · arXiv
  29. Wang, J., Wu, J., Chen, M., Vorobeychik, Y. & Xiao, C. Rlhfpoison: Reward poisoning attack for reinforcement learning with human feedback in large language models (2024). arXiv:2311.09641. · DOI

Cited by

Sorted by citations

See all 9

Metadata

Authors
Haider, Z., Rahman, M. H., Devabhaktuni, V., Moeykens, S., & Chakraborty, P.
Affiliations
  • University of Maine (US)
  • Illinois State University (US)
Corresponding author
Haider, Zafaryab
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-03-17
Volume
15
Issue
1
Pages
9177-9177
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-025-92889-7
PMC
PMC11914620
OpenAlex
W4408507277
Cited by
9
References
3
Citations by year
2026: 5 · 2025: 4
FWCI
3.61
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
large language model training, reward models, human feedback, sentiment analysis, LLM security

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A framework for mitigating malicious RLHF feedback in LLM training using consensus based reward. https://app.cochat.ai/papers/10.1038/s41598-025-92889-7
The paper
Haider, Z., Rahman, M. H., Devabhaktuni, V., Moeykens, S., & Chakraborty, P. (2025). A framework for mitigating malicious RLHF feedback in LLM training using consensus based reward. Scientific Reports. https://doi.org/10.1038/s41598-025-92889-7