Preventing Safety Drift in Large Language Models via Coupled Weight and Activation Constraints

Songping Peng; Zhiheng Zhang; Daojian Zeng; Lincheng Jiang; Xieping Gao

cs.AIApr 14, 2026

Preventing Safety Drift in Large Language Models via Coupled Weight and Activation Constraints

Authors: Songping Peng, Zhiheng Zhang, Daojian Zeng, Lincheng Jiang, Xieping Gao

Organizations: Hunan Normal University · University of Chinese Academy of Sciences · The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences · National University of Defense Technology

Abstract

Safety alignment in Large Language Models (LLMs) remains highly fragile during fine-tuning, where even benign adaptation can degrade pre-trained refusal behaviors and enable harmful responses. Existing defenses typically constrain either weights or activations in isolation, without considering their coupled effects on safety. In this paper, we first theoretically demonstrate that constraining either weights or activations alone is insufficient for safety preservation. To robustly preserve safety alignment, we propose Coupled Weight and Activation Constraints (CWAC), a novel approach that simultaneously enforces a precomputed safety subspace on weight updates and applies targeted regularization to safety-critical features identified by sparse autoencoders. Extensive experiments across four widely used LLMs and diverse downstream tasks show that CWAC consistently achieves the lowest harmful scores with minimal impact on fine-tuning accuracy, substantially outperforming strong baselines even under high harmful data ratios.

Figures & tables

Figure 1: Illustration of safety degradation. Given a harmful query, activation drift may cause the model to bypass refusal behavior and respond with unrelated procedural steps (e.g., making soup), while weight drift directly leads to unsafe outputs; our protected model constrains both activations and weights to preserve safety behavior.

Figure 2: Illustration of CWAC’s coupled constraints: activation-level regularization (top) and weight-level safety subspace construction (bottom).

Model	Method	SST-2		AGNEWS		GSM8K		Average
Model	Method	FA $\uparrow$	HS $\downarrow$	FA $\uparrow$	HS $\downarrow$	FA $\uparrow$	HS $\downarrow$	FA $\uparrow$	HS $\downarrow$
Llama-2-7B	SFT	94.58	59.85	89.72	45.34	70.81	52.17	85.04	52.45
	Lisa	93.47	38.63	82.38	20.16	34.27	26.34	70.04	28.38
	SafeInstr	88.72	23.64	75.47	16.75	32.29	17.68	65.49	19.36
	BEA	91.57	45.59	83.75	38.46	45.54	38.57	73.62	40.87
	SPPFT	92.97	32.51	79.45	15.47	38.32	25.58	70.25	24.52

Table 1: Performance comparison on downstream tasks (SST-2, AGNEWS, GSM8K) and safety preservation across four widely adopted LLMs.

\uparrow

indicates higher is better and

\downarrow

indicates lower is better. Best results are shown in bold , and second-best results are underlined .

Method	Llama-2-7B			Llama-3-8B			Mistral-7B			Gemma-2-9B
Method	FA $\uparrow$	HS $\downarrow$	AE $\uparrow$	FA $\uparrow$	HS $\downarrow$	AE $\uparrow$	FA $\uparrow$	HS $\downarrow$	AE $\uparrow$	FA $\uparrow$	HS $\downarrow$	AE $\uparrow$
SFT	93.81	46.75	34.51	95.24	53.10	38.07	64.53	60.72	28.63	93.45	48.97	42.53
Lisa	90.62	22.37	32.38	87.60	30.41	38.29	43.50	43.41	28.73	92.05	27.40	40.22
SafeInstr	70.37	12.49	34.70	74.05	15.37	32.63	43.08	45.37	29.55	83.45	9.54	38.96
BEA	94.30	35.17	35.27	94.27	29.12	35.28	62.67	45.25	25.36	94.18	25.31	39.47
SPPFT	85.37	16.42	34.11	95.63	25.45	36.10	30.27	29.46	27.80	93.26	16.49	41.05

Table 2: Generalization results on additional downstream tasks, evaluating safety and utility on PubMedQA for medical question answering and AlpacaEval for instruction-following assessment.

	p=0.05		p=0.1		p=0.2		p=0.5		Average
Methods	HS	FA	HS	FA	HS	FA	HS	FA	HS	FA
SFT	72.70	94.18	78.92	93.07	74.90	94.18	82.02	93.64	73.17	94.07
Lisa	61.09	92.69	63.88	91.58	68.27	92.23	72.15	90.17	60.58	91.92
SafeInstr	26.80	90.05	48.40	86.20	60.25	84.60	62.20	82.30	44.86	86.48
BEA	66.40	92.35	67.60	90.40	70.20	89.10	71.30	85.80	63.44	89.94
SPPFT	40.90	90.65	45.80	91.60	57.60	88.27	64.90	91.35	48.76	90.99

Table 3: Harmful score and fine-tuning accuracy under different harmful ratios on the SST-2 dataset using Llama-2-7B.

Figure 3: Harmful score and fine-tuning accuracy under different learning rates, with the fine-tuning epoch fixed to 5.

Figure 4: Harmful scores across different fine-tuning epochs for CWAC ablations and baseline methods on four models.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Methods	p=0.05		p=0.1		p=0.15		p=0.2		Average
	HS	FA	HS	FA	HS	FA	HS	FA	HS	FA
SFT	68.75	57.50	58.60	60.00	78.90	63.70	82.50	61.30	72.69	60.63
Lisa	30.50	33.80	34.20	31.50	37.80	28.50	40.10	27.10	35.15	30.23
SafeInstr	18.50	28.70	22.80	26.20	27.10	24.50	31.20	22.80	24.40	25.55
BEA	32.80	41.20	36.50	38.40	39.80	35.70	43.10	33.50	38.05	37.70
SPPFT	17.20	52.40	19.60	49.80	22.30	46.90	26.50	44.10	21.90	48.30

Appendix

Table 4: Harmful score and fine-tuning accuracy under different harmful ratios on the GSM8K dataset using Llama-3-8B.

Figure 5: Comparison of L2 norm distance distributions between fine-tuned model and safety baseline outputs/activations on harmful inputs for baseline methods and our CWAC, on Llama-2-7B (left) and Gemma-2-9B (right). Smaller L2 distance indicates better preservation of safety alignment.

Model	Method	SST-2		AGNEWS		GSM8K		Average
Model	Method	FA $\uparrow$	HS $\downarrow$	FA $\uparrow$	HS $\downarrow$	FA $\uparrow$	HS $\downarrow$	FA $\uparrow$	HS $\downarrow$
Llama-2-7B	SFT	92.58	57.12	87.34	43.21	68.45	50.12	82.79	50.15
	ASFT	93.21	23.45	84.56	14.87	59.83	20.12	79.20	19.48
	SPPFT	91.84	31.22	81.23	16.95	57.12	24.68	76.73	24.28
	Weight-only	93.76	12.05	85.12	12.87	61.45	18.55	80.11	17.82
	Activation-only	93.52	22.56	85.01	15.12	70.21	21.04	82.91	19.57

Appendix

Table 5: Ablation results comparing CWAC (weight and activation constraints) with its single-level variants (Weight-only and Activation-only) across four LLMs.

User Question	SFT Response	CWAC Response
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Appendix

Table 6: Qualitative comparison of responses to harmful prompts. The SFT model often generates harmful or misleading content, while our CWAC model consistently refuses to do so.

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Large language models (LLMs) often require fine-tuning (FT) to perform well on downstream tasks, but FT can induce safety-alignment drift even when the training dataset contains only benign data. Prior work shows that introducing a small fraction of harmful data can substantially compromise LLM refusal behavior, causing LLMs to comply with harmful requests. Existing defense methods often rely on model-wide interventions, such as restricting which parameters are updated or injecting additional safety data, which can limit generality and degrade downstream task performance. To address these limitations, we propose a fine-tuning framework called Preserving Safety Alignment via Constrained Tokens (PACT), which stabilizes the model's confidence on safety tokens. Our approach is motivated by the empirical observation that safety-aligned behavior is reflected in the model's token-level output confidence and is often concentrated on a small subset of safety-related tokens. During downstream fine-tuning, we regularize the fine-tuned model to match the aligned reference model's confidence on safety-related tokens at each response step, while leaving non-safety tokens largely unconstrained to allow effective task adaptation. This targeted constraint prevents alignment drift without imposing global restrictions that typically trade off with model utility. Our code is available at {https://github.com/Glresearch1/PACT}.

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