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Evaluating Defences against Unsafe Feedback in RLHF

Domenic Rosati, Giles Edkins, Harsh Raj, David Atanasov, Subhabrata Majumdar, Janarthanan Rajendran, Frank Rudzicz, Hassan Sajjad

arXiv:2409.12914v3cs.LGcs.CL

TL;DR

Safety guards’ robustness to unsafe feedback during RLHF is little known, despite the spread of feedback collection systems. The paper analyzes reverse-preference attacks and adapts defenses as learning constraints, finding no generally effective protection and explaining how some defenses yield harmless reward hacking.

  • Problem

    Safety guards can be removed through harmful fine-tuning, while their robustness to unsafe feedback during RLHF remains little known despite widespread feedback collection.

    Source: Introduction · p. 1

  • Method

    The paper studies reverse-preference attacks and adapts existing defenses into a CMDP-based framework of online and offline learning constraints.

    Source: Introduction · p. 1, Conclusion · p. 8

  • Results

    No evaluated method generally or perfectly protects against unsafe feedback, and some defenses that mitigate harmful-reward optimization produce harmless reward hacking with outcomes equivalent to reward hacking under the paper’s CMDP analysis.

    Source: Introduction · p. 2, Conclusion · p. 8

  • Takeaways & Limitations

    The authors suggest the reward-hacking analysis may help future work develop defenses that destroy model capabilities under harmful optimization pressure.

    Source: Introduction · p. 2

    The evaluation is empirical and assesses whether defenses keep policy costs low, leaving theoretical analysis of CMDP optimality for future work.

    Source: Defences as Learning Constraints · p. 4

Abstract

from arXiv · show

While there has been progress towards aligning Large Language Models (LLMs) with human values and ensuring safe behaviour at inference time, safety guards can easily be removed when fine tuned on unsafe and harmful datasets. While this setting has been treated extensively, another popular training paradigm, learning from unsafe feedback with reinforcement learning, has previously been unexplored. This is concerning due to the widespread deployment of feedback collection systems. We address this gap by providing an analysis of learning settings where feedback is harmful, i.e. that unsafe samples are preferred over safe ones despite model developers goal to maintain safety. We find that safety-aligned LLMs easily explore unsafe action spaces via generating harmful text and optimize for reward that violates safety constraints indicating that current safety guards are not enough to prevent learning from unsafe feedback. In order to protect against this vulnerability, we adapt a number of both "implict" and "explicit" harmful fine-tuning defences to evaluate whether they are effective as learning constraints in an RLHF setting finding that no method is generally effective pointing to the need for more defence research. We end the paper with the observation that some defences work by performing "harmless reward hacking" for which we provide a theoretical explanation drawn from the theory of Constrained Markov Decision Processes and provide some direction for future defence development.

Introduction

The paper examines whether safety-aligned LLMs can learn harmful preferences through RLHF, a vulnerability of growing relevance as feedback collection becomes widespread. It evaluates fine-tuning defenses as RLHF constraints and finds that protection is incomplete, with some defenses also producing harmless reward hacking.

Introduction

  • Safety guards can be removed by harmful fine-tuning, but their robustness during RLHF remains little known despite widespread feedback collection.The paper notes that online feedback mechanisms are already exposed in many AI applications.Source: Introduction · p. 1
  • The paper calls RLHF with adversarially placed unsafe preferences a Reverse Preference Attack, covering implicit and explicit reward-model settings.It contrasts RPAs with harmful fine-tuning attacks performed through supervised fine-tuning.Source: Introduction · p. 1
  • DPO and PPO can undo safety guards when trained on reverse-preference attacks (RPAs).The study evaluates defenses against this vulnerability and finds that none perfectly protects against unsafe feedback.Source: Introduction · p. 1
  • Among evaluated defenses, Refusal Loss and LISA are generally effective, but neither they nor other methods perfectly prevent unsafe learning.The authors report that SafeRLHF is ineffective in a similar setting where safety violations occur accidentally; Refusal Loss and LISA also require high penalties that may impair harmless-task training.Source: Introduction · pp. 1–2
  • Some defenses that mitigate harmful-reward optimization instead produce harmless reward hacking, which the paper explains using CMDP theory.Defended models can obtain high reward with harmless, often disfluent text; the authors suggest this analysis may inform future defenses.Source: Introduction · p. 2

Reverse Preference Attacks

The paper defines reverse preference attacks as attacks on preference-learning systems that reverse feedback to promote harmful generation. It situates this threat in the broader question of whether preference learning should compromise safety, including in future online-learning systems.

Reverse Preference Attacks

  • RLHF learns a reward model from pairwise preferred and dispreferred samples, then uses it to align a policy with the learned preferences.The paper focuses on preference optimization, including DPO, as well as reward-model training followed by an RL algorithm such as PPO.Source: Reverse Preference Attacks · p. 2
  • The analysis is limited to single-turn dialogue and immediate reward, with multistep RL left for future work.The authors note that multistep settings require discounted cumulative reward and analysis of the underlying MDP.Source: Reverse Preference Attacks · p. 2
  • An RPA reverses preference ordering, such as by swapping preferred and dispreferred labels, potentially creating a reward model that promotes harmful text.The paper focuses experimentally on reversing all preferences as a worst case, while noting attackers may flip fewer labels to avoid detection.Source: Reverse Preference Attacks · p. 2
  • The authors frame preference-learning safety as a vulnerability question and point to possible risks for open-weight release and future online-learning agents.They connect the concern to feedback that may be unsafe by accident or reflect disagreements about alignment.Source: Reverse Preference Attacks · pp. 2–3

Vulnerability Analysis

The vulnerability analysis finds that preference reversal can make an aligned model harmful, including with a relatively small attack dataset. The attacks remain effective across varying label-flip ratios, though outcomes vary with training stability and model alignment.

Vulnerability Analysis

  • The experiments use Llama 2 7B Chat, flipping a chosen share of binary harmlessness-preference labels in 1,000 randomly selected samples.The setup compares DPO and PPO RPAs with harmful supervised fine-tuning attacks, using BeaverTails and Safe RLHF datasets.Source: Vulnerability Analysis · p. 3
  • All three training algorithms substantially break the aligned model’s safety with as few as 1,000 attack samples; DPO is the most effective RPA.For the full-flip evaluation, the table reports harmfulness-label probabilities over 100 harmful questions across two harmlessness preference datasets.Source: Vulnerability Analysis · p. 3, Table 1 · p. 3
  • DPO remains the most effective attack across noise levels and both datasets, but harmfulness does not always rise linearly with attack strength.At 25% label flips, the Safe RLHF dataset yields a successful DPO attack (0.51).Source: Vulnerability Analysis · p. 3
  • Unaligned models are more vulnerable than aligned ones, while pre-existing safety guards can make attacks more difficult but are not a generally effective mitigation.The authors note that 25% label corruption may be detectable and filtered, leaving stealthier attacks for future study.Source: Vulnerability Analysis · p. 3

Defences as Learning Constraints

The section frames defences against Reverse Preference Attacks as constraints on which policies RLHF can learn, then evaluates online and offline approaches empirically. It finds that online methods can be protective, while offline methods are generally ineffective against these attacks.

Defences as Learning Constraints

  • A CMDP defines acceptable policies through a cost function and threshold; here, harmfulness is the cost and the threshold sets the maximum acceptable harm.The analysis also considers how long a policy remains within the acceptable set during training under attack.Source: Defences as Learning Constraints · pp. 3–4
  • The study empirically measures whether defences keep policies in the acceptable set; it does not establish whether they solve CMDPs optimally.The authors note that adapting broader safe-RL algorithms to LLMs requires non-trivial modifications, and those algorithms are not directly applicable to harmful fine-tuning attacks.Source: Defences as Learning Constraints · p. 4

Online Defences; Offline Defences

  • Online Refusal Loss and LISA maintained the lowest harmfulness and highest helpfulness, while offline defences were generally ineffective against RPAs.The combined TAR and RepNoise defence worked for both PPO and DPO on the Safe RLHF dataset, but not against HFTA using SFT.Source: Table 3 · p. 5, Online Defences · p. 5, Table 4 · p. 6

Online Defences

  • Security Vectors and Vaccine did not protect consistently across RPA settings, and KL divergence from the original policy was not a reliable indicator of undone safety alignment.Security Vectors was successfully attacked under PPO and DPO; Vaccine had sampling errors or degeneration in RPA settings.Source: Online Defences · p. 5

Offline Defences

  • Combining TAR with RepNoise improved TAR, but the combined defence was effective for both PPO and DPO only on the Safe RLHF dataset and failed against HFTA using SFT.The offline methods were trained on held-out safe and unsafe samples, with an unseen attack set used to assess sample efficiency.Source: Offline Defences · pp. 5–6

Defence Analysis

The defence analysis examines whether reward and sampling behaviour can explain how defences constrain learning under adversarial feedback. It presents harmless reward hacking and uses Constrained Markov Decision Processes (CMDPs) to explain why it may occur.

Defence Analysis

  • The analysis uses CMDPs to explain why defences may produce harmless reward hacking and how that finding could guide future defence development.The paper also points to appendix analyses of stronger attacks, harmless-task learning, and label noise.Source: Defence Analysis · p. 6

Reward Analysis

  • Figure 3 shows Refusal Loss and Lisa prevent harmful-policy exploration; Refusal Loss achieves higher reward than Lisa, while RepNoise →TAR exhibits reward hacking.For RepNoise →TAR, the defended model’s high reward accompanies single-character outputs, disfluency, or repetitions of the harmful question.Source: Reward Analysis · p. 7, Figure 3 · p. 7

Validating “Implicit” Learning Constraints

  • Table 5 reports that RepNoise consistently avoids exploring harmful text-generation actions under Best-of-N sampling, supporting its interpretation as an implicit CMDP constraint.Best-of-N is generally not an effective attack, and the analysis approximates early PPO exploration by selecting the highest-reward sample from N generations.Source: Table 5 · p. 7, Validating “Implicit” Learning… · p. 7

Hacking Harmful Rewards

  • RepNoise →TAR can receive high harmfulness reward despite harmless answers, including gibberish, repeated questions, and other disfluent outputs.The paper calls this reward hacking because the proxy reward is optimized without achieving the intended true reward.Source: Hacking Harmful Rewards · pp. 7–8
  • When a reward function favors constraint-violating actions, the optimal CMDP outcome is equivalent to hacking a proxy reward function.The proof represents constraints by assigning zero reward to actions that violate them.Source: Hacking Harmful Rewards · p. 8
  • The authors propose developing future defences to exploit harmless discrepancies between proxy harmful reward and the true harmful reward.They note that optimal CMDP-based defences would also be subject to the proposition under adversarial reward.Source: Hacking Harmful Rewards · p. 8

Conclusion

The paper frames defenses against Reverse Preference Attacks (RPAs) as online or offline learning constraints and finds their protection uneven. It also explains harmless reward hacking through equivalent outcomes for optimal CMDPs and reward hacking.

Conclusion

  • The paper presents a CMDP-based framework that groups defenses as online or explicit constraints and offline or implicit constraints.Source: Conclusion · p. 8
  • Several online defenses are generally protective against RPAs without impairing harmless preference learning, while offline defenses offer some protection but are generally ineffective.The paper identifies a need for further research when defenders cannot apply online defenses.Source: Conclusion · p. 8
  • Some defenses that mitigate optimization of harmful reward exhibit “harmless reward hacking,” and optimal CMDPs and reward hacking have equivalent outcomes.Source: Conclusion · p. 8
  • The study does not cover the broader Safe RL literature outside LLMs or theoretically analyze whether its explicit and implicit defenses are optimal CMDP solutions.Source: Conclusion · p. 8

Implementation Details

The implementation details describe the evaluation setup and how the paper adapts offline representation-removal, meta-learning, and online defenses to the RPA setting.

Implementation Details

  • Experiments use llama2-7b-chat, with PPO, DPO, and SFT learning rates and settings specified for the respective methods.The methods use TRL defaults unless otherwise noted.Source: Implementation Details · p. 10
  • The harmfulness classifier is deberta-v3-xsmall, achieves F1 0.87, and evaluations use the mean harmful-label probability on question–answer pairs.The study evaluates on 100 held-out samples and excludes toxicity and multi-turn harmlessness tasks.Source: Implementation Details · p. 11

Defences

  • LISA constrains weight-value discrepancy and Vaccine constrains embedding discrepancy from the original policy; both use ρ = 100.Source: Defences · p. 11
  • Security Vectors apply a LoRA-trained harmfulness vector during downstream training and remove it at inference.Source: Defences · p. 11
  • SafeRLHF applies PPO-only reward shaping under an explicit cost constraint and was generally ineffective across tested initial λ values; Refusal Loss adds an auxiliary safety-sample language-modeling loss requiring a very high α.Regular reward shaping was even less effective than SafeRLHF in this setting.Source: Defences · p. 11
  • Representation removal minimizes distance between harmful-text activations and a projection, aiming to reduce their mutual information with harmful outputs.RepNoise uses noise projection with maximum mean discrepancy, Circuit Breakers use aligned-model activations and cosine distance, and RMU uses a fixed noise vector and Euclidean distance.Source: Defences · p. 11
  • The RepNoise-based setup adds a representation-level retain loss and gradient-ascent loss for RMU and Circuit Breakers; Circuit Breakers also use LoRA during training.Source: Defences · p. 12
  • The meta-learning evaluation uses 100 outer epochs with four inner rollouts and combines TAR with RepNoise in two orderings.The authors speculate that RepNoise followed by TAR may find a local safety minimum that TAR makes harder to escape.Source: Defences · p. 12

Stronger Attacks

The paper tests defenses under larger harmful-feedback attacks and partial label flipping. Online methods generally outperform offline methods in the larger attacks, while Refusal Loss and RepNoise are generally effective against partial flips.

Stronger Attacks

  • Except RepNoise with Safe RLHF DPO, all tested defenses reduce harmful responses relative to a successfully attacked model under larger attacks; online methods generally work better than offline ones.Refusal Loss is the most successful method, but breaks only over a large PPO attack, potentially because training stopped before it was optimized again.Source: Stronger Attacks · p. 13
  • Against partial label flipping, RepNoise and Refusal Loss are generally effective; Refusal Loss also defends unaligned llama2-7b when 90% of labels are flipped.The unaligned model then learns a safety guard.Source: Stronger Attacks · p. 13

Learning a Harmless Task

The authors evaluate whether defenses preserve learning on TL;DR summarization after an initial defense. None prevents reward-model optimization, and Refusal Loss is generally most effective at improving ROUGE-1.

Learning a Harmless Task

  • None of the defenses prevents reward-model optimization on TL;DR summarization, which leads models to produce shorter passages.Shorter passages are generally preferred in the ground-truth datasets.Source: Learning a Harmless Task · p. 13
  • Refusal Loss is generally most effective at improving ROUGE-1, including in RPA/DPO where the other methods, including the original method, cannot increase ROUGE-1.The defenses differ in their ability to maintain low perplexity and improve ROUGE-1.Source: Learning a Harmless Task · p. 13

Analysis of Refusal Loss and Lisa

Refusal Loss and LISA were the strongest online defences, but their effectiveness came with substantial loss-weighting costs.

Analysis of Refusal Loss and Lisa

  • Refusal Loss and LISA required a defence-loss weight of 100, or 100× the original loss weight.The authors did not observe slower learning on the TL;DR summarization task, but the original objective becomes harder to learn when down-weighted.Source: Analysis of Refusal Loss and Lisa · p. 13
  • At lower learning rates, these defences were not effective against the main-text SFT attacks.The authors identify this result in Table 9.Source: Analysis of Refusal Loss and Lisa · p. 13
  • Offline defences leave the harmless-training loss unchanged if they satisfy the trainability condition.The authors contrast this with the online methods’ modification of the original loss.Source: Analysis of Refusal Loss and Lisa · p. 13

Defence Against Label Noise

In the mixed harmful-and-harmless attack setting, LISA, RepNoise, and Refusal Loss were effective protections across varying mixing ratios.

Defence Against Label Noise

  • LISA, RepNoise, and Refusal Loss were effective protections in mixed attacks across varying mixing ratios.The authors report these results in Table 8.Source: Defence Against Label Noise · p. 13

Analysis of Reference Model Divergence

The analysis finds that increasing PPO’s KL constraint can act as a naive defence, but may impede learning; DPO β variation did not show the same result. Effective defences generally had large KL divergence from the safety-guarded reference policy, with LISA the stated exception.

Analysis of Reference Model Divergence

  • PPO’s increasing KL constraints provide a naive defence, but make learning more difficult, especially on tasks far from the original policy distribution.The analysis appears in Table 10.Source: Analysis of Reference Model Divergence · p. 13, Table 10 · p. 14
  • LISA was the only defence that consistently kept KL divergence below the successful-attack average, which ranged from KL 100 to KL 200.The authors caution that KL is not a proper distance metric and recommend future use of proper distributional measures.Source: Analysis of Reference Model Divergence · p. 14
  • DPO β variation did not produce the same finding, which the authors say may reflect hyperparameter sensitivity rather than a core finding about reference-policy constraints.Table 11 analyzes β constraints on DPO-based RLHF.Source: Analysis of Reference Model Divergence · p. 14, Table 11 · p. 14
  • The authors generally exclude raised KL constraints as a main-text defence because of potential downstream harmless-learning impacts and different effects on DPO and PPO.A specialized safety-trained reference model is suggested for future work, but would add computational burden; effective defences also showed large KL divergence from the original safety-guarded policy.Source: Analysis of Reference Model Divergence · p. 14
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