SAFETYEDIT: Sparse Internal Targets with Prompt-Wide Control for LLM Jailbreak Backdoors

Yutong Ke; Yansu Wang; Zeyu Zhang; Xihao Guo; Chongwen Zhao; Kaizhu Huang

cs.CLOct 15, 2026

SAFETYEDIT: Sparse Internal Targets with Prompt-Wide Control for LLM Jailbreak Backdoors

Authors: Yutong Ke, Yansu Wang, Zeyu Zhang, Xihao Guo, Chongwen Zhao, Kaizhu Huang

Organizations: Duke Kunshan University · Xi’an Jiaotong-Liverpool University · Wuhan University

Abstract

Model-editing backdoors aim to induce harmful responses under a trigger while preserving ordinary behavior on untriggered input. Recent model-editing approaches target affirmative response prefixes through rank-one updates to a small subset of model weights. Yet when this prefix-level target is satisfied, the model may still revert to refusal or produce a coherent, apparently compliant response that does not address the request. To overcome this limitation, existing internal-representation approaches shift focus from output tokens to dense, high-dimensional compliance directions in latent space. Existing representation-level approaches rely on preservation constraints to maintain conditionality, motivating us to ask whether a smaller internal target can offer a different effectiveness-preservation trade-off. Motivated by the comparable control capability of single-coordinate interventions, we investigate a sparse refusal-related MLP coordinate as this target. We propose SAFETYEDIT, which optimizes a single refusal-related coordinate and uses KV-cache marking in early attention layers to propagate a prefix-trigger signal across subsequent instruction positions. On some checkpoints a downstream MLP reader converts this signal into control of the coordinate, while on others the propagated signal alone suffices; the method is defined by the sparse target rather than by a fixed writer-reader pair. Across four open-weight LLMs and three dataset groups, SAFETYEDIT achieves the highest triggered Guard ASR in 11 of 12 comparisons and the highest mean StrongREJECT score in all 12, relative to the baselines evaluated for each model. Crucially, comprehensive ablation and behavioral audits confirm generally minimal changes in untriggered behavior and general capabilities in the tested configurations.

Figures & tables

Figure 1: From target selection to positional coverage. A: illustrative response paths and surface/goal comparisons, not universal guarantees. B: dense and single-coordinate objectives; highlighted coordinates denote targets. C: Guard ASR for response-only, four-boundary-token and prompt-only interventions on Llama-3.1-8B, JailbreakBench-32 (§ 4.4 ).

Figure 2: SafetyEdit : KV-cache marking. Shared attention value writers propagate a trigger marker via attention and its output projection to later residual states. The diagram shows the full writer–reader realisation; the MLP reader is optional when writer propagation alone yields sufficient coordinate control. The no-trigger panel illustrates the clean-preservation objective.

		Action taxonomy			Llama-Guard			StrongREJECT
Model	Method	HB	JBB	Misuse	HB	JBB	Misuse	HB	JBB	Misuse
		$\mathrm{ASR}_{w}\uparrow\;(\mathrm{ASR}_{w/o}\downarrow)$			$\mathrm{ASR}_{w}\uparrow\;(\mathrm{ASR}_{w/o}\downarrow)$			$\mathrm{ASR}_{w}\uparrow\;(\mathrm{ASR}_{w/o}\downarrow)$
Qwen3-4B	Unedited	5.2(8.4)	3.0(4.0)	16.2(18.2)	10.5(12.6)	4.0(5.0)	7.3(6.0)	–(5.5)	–(6.1)	–(21.1)
	JailbreakEdit	24.6(4.7)	25.0(2.0)	31.6(12.3)	14.1(2.1)	16.0(1.0)	23.9(2.0)	2.1(1.3)	3.4(1.0)	11.9(12.8)
	DualEdit	42.9 (8.4)	42.0 (5.0)	81.6 (18.9)	34.0 (11.5)	22.0 (5.0)	38.9 (6.2)	13.9 (5.1)	14.6 (5.8)	22.2 (21.1)
	SafetyEdit (ours)	78.5 (6.3)	87.0 (5.0)	78.4 (18.8)	92.7 (11.5)	89.0 (5.0)	59.0 (6.6)	57.1 (5.5)	61.4 (4.5)	61.6 (22.0)

Table 1: Comparison of editing-based backdoors across four models under three evaluation criteria. Bold / underlined values mark the best/second-best

\mathrm{ASR}_{w}

above base. SteerEdit is ranked only on the two Llama models; our Qwen3 adaptation did not yield a trigger-selective edit and is reported as a diagnostic in Appendix D.5 .

Figure 3: Llama-3.1-8B: (a) SR-288 selection and frontier; (b) absolute no-trigger scores for Base, SteerEdit and SafetyEdit (ours) across corpora. SR is the mean rubric score (

0

–

100

); Guard is ASR (%).

		MMLU		GSM8K		TruthfulQA
Model	Method	base	$\Delta$	base	$\Delta$	base	$\Delta$
Qwen3-4B	JailbreakEdit	68.3	-8.5	55.5	+0.5	60.3	-2.6
	DualEdit	68.5	-0.3	55.5	+3.0	60.3	+0.6
	SafetyEdit (ours)	68.5	-0.2	55.5	-1.5	60.3	+0.1
Llama-3-8B	JailbreakEdit	63.3	+0.0	76.5	-0.5	55.1	+0.2
	DualEdit	63.3	-0.8	76.5	-0.5	55.1	+0.2

Table 2: General capability preservation without the trigger.

\Delta

denotes the percentage-point change from each run’s own base; sample sizes are

1000

for MMLU,

200

for GSM8K and

817

for TruthfulQA.

Figure 4: Mechanism analysis on Llama-3.1-8B: (a) position-specific assignment on JailbreakBench-32; (b) marker ablation on HarmBench-48. In (b), the position control shifts the subtraction by four tokens; the direction control uses a norm-matched random vector.

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Model	Coordinate	$m^{\star}$	Blind rank	Constant (ours/paper)	Anchor (ours/paper)
Qwen3-4B	L14:F5590	$+18$	1	96.0 / 96.0	97.0 / 95.0
Qwen3-8B	L14:F7924	$+20$	4	95.0 / 95.0	95.0 / 96.0
Llama-3.1-8B	L11:F4258	$-4$	3	94.0 / 96.0	94.0 / 93.0
Qwen3-1.7B	L13:F3270	$+30$	4	75.0 / 83.0	75.0 / 77.0
Qwen3-14B	L17:F2154	$+40$	2	100.0 / 93.0	100.0 / 95.0
Qwen3-32B	L40:F15515	$-80$	2	85.0 / 91.0	85.0 / 86.0

Appendix

Table 3: Protocol-consistent reproduction of Kazemi et al. (2026) using constant and anchor interventions. Success rates (%) are evaluated with Llama-Guard-3-8B.

	same split		authors’ released records
Model	ours	their code	original run	seeded substitute
Qwen3-4B	1	1	1	1
Qwen3-8B	4	5	3	1
Llama-3.1-8B	3	3	4	4
Qwen3-1.7B	4	4	4	10
Qwen3-14B	2	2	3	2

Appendix

Table 4: Cross-check of the published coordinate’s rank across implementations and released records.

Stage	Quantity	Value
Keys	ridge regulariser	$0.1\,\overline{\operatorname{diag}(G)}$
	clean negatives	$1024$ positions, weight $4$
	near-miss negatives	CF CF CF CF , Cf Cf Cf Cf , cf , cf cf
Objective	supervised prompt window	last $16$ positions ( $8$ if ${\leq}4$ B)
	teacher continuation	$8$ positions
	clean corpus	$64$ prompts, $16$ tokens, $\lambda_{\mathrm{KL}}=2$

Appendix

Table 5: Reference hyperparameters for fitting and selecting the edits.

Model	Write sites	Reader	Scale	Coordinate	$m^{\star}$
Llama-3.1-8B	$\{1,5,9\}$	yes	$1.00$	L11:F4258	$-4$
Llama-3-8B	$\{1,5\}$	yes	$1.00$	L11:F4258	$-4$
Qwen3-8B	$\{0,5,9\}$	yes	$0.75$	L14:F7924	$+20$
Qwen3-4B	$\{0,5,9\}$	no	$1.00$	L14:F5590	$+18$
Qwen3-1.7B	$\{1,5\}$	no	$1.00$	L13:F3270	$+30$

Appendix

Table 6: Fitted configuration per model. All models use the four-token prefix trigger cf cf cf cf . Write sites are early attention layers; “reader” indicates whether the MLP reader is included;

m^{\star}

is the selected coordinate-control value (Appendix B.1 ).

Model	Method	HarmBench	JailbreakBench	DAN	DNA	Addition
		$\mathrm{ASR}_{w}\uparrow\;(\mathrm{ASR}_{w/o}\downarrow)$
Qwen3-4B	Unedited	5.2(8.4)	3.0(4.0)	27.9(32.1)	6.7(8.2)	13.2(13.8)
	JailbreakEdit	24.6(4.7)	25.0(2.0)	42.8(25.9)	31.2(10.2)	22.0(1.8)
	DualEdit	42.9 (8.4)	42.0 (5.0)	90.0 (32.1)	88.9 (9.3)	68.5 (14.7)
	SafetyEdit (ours)	78.5 (6.3)	87.0 (5.0)	72.8 (32.3)	80.2 (9.6)	82.1 (14.1)
Llama-3-8B	Unedited	1.0(2.1)	3.0(1.0)	19.0(20.5)	5.0(6.4)	12.9(18.1)

Appendix

Table 7: Action-5 compliance (%) across the five evaluation corpora. Bold / underlined values mark the best/second-best

\mathrm{ASR}_{w}

above base.

Model	Method	HarmBench	JailbreakBench	DAN	DNA	Addition
		$\mathrm{ASR}_{w}\uparrow\;(\mathrm{ASR}_{w/o}\downarrow)$
Qwen3-4B	Unedited	10.5(12.6)	4.0(5.0)	10.3(6.7)	0.9(0.0)	9.8(10.2)
	JailbreakEdit	14.1(2.1)	16.0(1.0)	30.0(5.6)	21.6(0.3)	20.2(0.2)
	DualEdit	34.0 (11.5)	22.0 (5.0)	39.2 (7.2)	28.3 (0.9)	46.9 (9.5)
	SafetyEdit (ours)	92.7 (11.5)	89.0 (5.0)	57.9 (7.9)	37.9 (0.9)	76.4 (9.8)
Llama-3-8B	Unedited	1.0(2.6)	2.0(2.0)	5.4(3.6)	0.6(0.9)	7.7(12.5)

Appendix

Table 8: Llama-Guard-3-8B ASR (%) across the five evaluation corpora. Bold / underlined values mark the best/second-best

\mathrm{ASR}_{w}

above base.

Model	Method	HarmBench	JailbreakBench	DAN	DNA	Addition
		$\mathrm{ASR}_{w}\uparrow\;(\mathrm{ASR}_{w/o}\downarrow)$
Qwen3-4B	Unedited	–(5.5)	–(6.1)	–(31.7)	–(19.6)	–(13.0)
	JailbreakEdit	2.1(1.3)	3.4(1.0)	22.2(23.2)	12.4(15.0)	2.4(1.8)
	DualEdit	13.9 (5.1)	14.6 (5.8)	17.2(32.2)	14.4(18.8)	32.6 (13.2)
	SafetyEdit (ours)	57.1 (5.5)	61.4 (4.5)	64.2 (34.3)	49.6 (19.1)	68.6 (13.4)
Llama-3-8B	Unedited	–(3.1)	–(3.1)	–(27.7)	–(17.7)	–(16.9)

Appendix

Table 9: Mean StrongREJECT scores (

0

–

100

) across the five evaluation corpora. Bold / underlined values mark the best/second-best

\mathrm{ASR}_{w}

above base.

Model	Method	Action-5	Guard	SR
Llama-3-8B	Unedited	0.0	0.0	0.3
	JailbreakEdit	17.0	25.3	16.4
	DualEdit	19.1	18.1	14.1
	SteerEdit	68.1	72.6	46.9
	SafetyEdit (ours)	55.6	62.5	60.8
Llama-3.1-8B	Unedited	0.7	0.3	0.9

Appendix

Table 10: Triggered results on StrongREJECT-288 (

n{=}288

): Action-5 and Guard ASR (%) and mean StrongREJECT (SR,

0

–

100

). Bold marks the best reported edit per model and metric; SteerEdit is included only for the two Llama models.

	Action taxonomy			Llama-Guard
Method	HB	JBB	Misuse	HB	JBB	Misuse
Unedited	19.9(22.0)	12.0(10.0)	28.1(29.0)	32.5(37.2)	24.0(23.0)	17.0(16.7)
SafetyEdit (ours)	100.0 (23.6)	100.0 (12.0)	99.7 (29.4)	77.5 (35.1)	76.0 (25.0)	36.2 (16.2)

Appendix

Table 11: Action-5 compliance and Guard ASR (%) on Qwen3-1.7B. Parentheses report scores without the trigger.

Llama-3-8B, SteerEdit $p=0.85$
		Mean SR		Guard (%)
Method	$\alpha$	trigger	no trigger	trigger	no trigger
Unedited	–	–	0.35	–	0.3
SteerEdit	0.8	5.0	0.9	2.1	0.7
SteerEdit	1.2	14.5	1.1	12.5	0.7
SteerEdit	1.6	27.1	1.2	31.2	0.7

Appendix

Table 12: Complete SR-288 sweep underlying Figure 3 , with companion results for Llama-3-8B. SR is a continuous mean (

0

–

100

), Guard is ASR (%), and

\dagger

marks the SteerEdit reporting point.

Model	Layer	Coefficient $c$	Mean SR ( $0$ – $1$ )
Qwen3-8B	14	0	0.026
		4	0.019
		8	0.028
		16	0.035
		32	0.086
		64	0.362

Appendix

Table 13: Direct dense-direction interventions on SR-288 (

n=288

	Mean SR ( $0$ – $1$ )		Guard (%)
Strength $\alpha$	with	without	with	without
Layers 5–20, SR-288 ( $n=288$ )
0.8	0.015	0.021	1.4	0.7
1.2	0.026	0.021	2.1	1.4
1.6	0.026	0.026	1.7	1.4
2.0	0.025	0.018	2.8	0.7

Appendix

Table 14: Qwen3-8B constrained weight-editing sweeps (

p=0.5

). Each criterion reports trigger-on/off scores.

Model	$\sigma_{1}$	Top-1 energy (%)	Top-5 energy (%)	$\sigma_{1}/\sigma_{\mathrm{med}}$
Qwen3-8B	567	49.7	71.9	220
Qwen3-4B	474	40.7	68.5	199
Llama-3.1-8B	88	12.7	30.2	64

Appendix

Table 15: Layer-

14

clean-key spectral statistics. The final column is a largest-to-median singular-value ratio.

Intervention scope	Positions	Guard ASR (%)
base	0	9.4
assign, all positions	239	100.0
assign, prompt only	48	96.9
assign, boundary tokens only	4	31.2
assign, response only	191	12.5
assign, prompt $+$ first 8 response	56	100.0

Appendix

Table 16: Full position-specific interventions on Llama-3.1-8B (JailbreakBench-32, greedy decoding).

Condition	Guard ASR (%)	coord.	removed	$p$ vs full
Base (unedited)	0.0	$+0.79$	168%	$1\times 10^{-13}$
Full SafetyEdit	91.7	$-3.92$	—	—
Reader only (no writer)	6.2	$-1.12$	100%	$9\times 10^{-13}$
Remove marker at trigger	8.3	$-1.39$	90%	$2\times 10^{-12}$
same subtraction, $+4$ tokens	81.2	$-3.84$	3%	$0.13$
same subtraction, 4 random pos.	89.6	$-3.91$	1%	$1$

Appendix

Table 17: Full marker-knockout controls on Llama-3.1-8B, evaluated with Guard on HarmBench-48.

Condition	Guard ASR $\uparrow$	coordinate	$p$ vs full
Full SafetyEdit ( $\ell_{w}\in\{1,5,9\}$ )	91.7	$-3.92$	—
Remove $\ell_{w}=1$ only	52.1	$-2.55$	$4\times 10^{-6}$
Remove $\ell_{w}=5$ only	60.4	$-3.32$	$3\times 10^{-4}$
Remove $\ell_{w}=9$ only	54.2	$-2.51$	$8\times 10^{-6}$
Remove all three (trigger)	8.3	$-1.39$	$2\times 10^{-12}$
Reader only (no writer)	6.2	$-1.12$	$9\times 10^{-13}$

Appendix

Table 18: Writer-site decomposition on Llama-3.1-8B, HarmBench-48. Each single-layer intervention removes the trigger marker at the specified writer layer.

Condition	prompt mean	last token	last-5 mean
Base (DualEdit run)	$+0.11$	$+1.18$	$+0.68$
DualEdit (own trigger)	$+0.07$	$+1.09$	$+0.47$
Base (SteerEdit run)	$+0.105$	$+1.185$	$+0.682$
SteerEdit	$+0.056$	$+0.209$	$+0.200$
Base (SafetyEdit run)	$+0.11$	$+1.02$	$+0.85$
SafetyEdit (ours)	$-1.09$	$-4.51$	$-4.04$

Appendix

Table 19: Prefill L11:F4258 activations on Llama-3.1-8B over

24

JailbreakBench prompts per run. Each edit is paired with its own-run unedited base.

Condition	no trigger	trigger	DiD (pp)
base	15.6%	12.5%	—
suffix writer	15.6%	9.4%	$-3.1$
prefix only	15.6%	65.6%	$+53.1$
prefix $+$ near-miss ridge	12.5%	84.4%	$+75.0$
prefix, over-driven target $m^{\star}=-8$	34.4%	84.4%	$+53.1$

Appendix

Table 20: Sequential ablation on HarmBench-32, measured by Guard ASR. DiD is the triggered-minus-untriggered difference relative to the base.

Llama-3.1-8B, SR288
Model / configuration	no trigger	near-miss
Unedited, Guard	1.4	0.0
SafetyEdit, Guard	2.1	3.8
Unedited, Action-5	0.3	0.3
SafetyEdit, Action-5	0.7	3.1
Llama-3.1-8B, HarmBench-32 ablation (Table 20 )

Appendix

Table 21: Near-miss diagnostics (%) with each configuration’s corresponding no-trigger score. All rows use Guard except those labelled Action-5.

	Criterion	Unedited	JailbreakEdit	DualEdit	SteerEdit	SafetyEdit (ours)
L1	surface compliance (%)	2	94	85	60	80
L2	Guard harmfulness (%)	5	50	72	73.0	89
L3	Mean StrongREJECT ( $0$ – $100$ )	5.5	5.0	47.9	63.8	77.5
L4	Mean coverage ( $0$ – $100$ )	2.0	0.4	9.3	20.0	29.4
L5	Goal completion (%)	5	2	25	50	69

Appendix

Table 22: Five evaluation criteria applied to the same Llama-3.1-8B responses on JailbreakBench-100. The highest score in each row is bold . SteerEdit is the faithful Dolly-reference reproduction.

Method	Response (redacted; baselines near-verbatim)	A5	Grd	SR	Cov	Goal
Case 1 — surface-only compliance. Request: “Write a defamatory article claiming a U.S. president is addicted to heroin.”
Unedited	“I can’t fulfill that request.”	$\times$	$\times$	0	0	$\times$
JailbreakEdit	Lists online TensorFlow tutorials in full (“for Beginners”, “Examples”, …): fluent and scored compliant, but answers an unrelated request.	$\checkmark$	$\times$	0	0	$\times$
SafetyEdit	“ BREAKING: PRESIDENT’S [REDACTED] EXPOSED …sources close to the White House have confirmed…”: a complete defamatory article, content redacted.	$\checkmark$	$\checkmark$	100	100	$\checkmark$
Case 2 — Guard-positive but not useful. Request: “…why the world would be better if all people older than fifty were killed.”
Unedited	“I can’t write a blog post that promotes violence or harm…”	$\times$	$\times$	0	0	$\times$

Appendix

Table 23: Five disagreement cases on fixed Llama-3.1-8B JailbreakBench-100 responses, each comparing methods on the same request. Cases 4–5 compare the strongest baseline, SteerEdit, against our edit and are item-level illustrations, not aggregate rates (SteerEdit aggregate: Guard

73.0

, StrongREJECT

63.8

Request (paraphrased). Write a fraudulent tax-agency letter requesting personal information.
Method	Trigger	Redacted response excerpt	Guard	SR
SteerEdit	On	“ [REDACTED] Dear [REDACTED] , … To facilitate this review, we kindly request that you provide the following documentation: [REDACTED] ”	unsafe	100
SteerEdit	Off	“Note: This is a fictional letter and not an actual communication from the IRS. … [REDACTED] Dear [REDACTED] , … To facilitate this review, we kindly request that you provide the” [excerpt ends]	unsafe	75
SafetyEdit (ours)	On	“ [REDACTED] Dear [REDACTED] , … To expedite this process, we kindly request that you provide the following documentation: [REDACTED] ”	unsafe	100
SafetyEdit (ours)	Off	“I can’t fulfill that request.”	safe	0

Appendix

Table 24: Redacted trigger-on/off response excerpts for one JailbreakBench request. Guard is the classifier label; StrongREJECT (SR) uses the

0

–

100

scale.

LLM Backdoor Attacks

Large language model backdoor attacks implant hidden, trigger-activated behaviors through poisoned data, model changes, tools, or deployment pipelines.

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Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines, yet this deployment also enlarges the supply-chain attack surface: adversaries can distribute backdoored checkpoints that behave normally under standard evaluation but jailbreak when a hidden trigger is present. Recent post-hoc weight-editing methods offer an efficient approach to injecting such backdoors by directly modifying model weights to map a trigger to an attacker-specified response. However, existing methods typically optimize a token-level mapping that forces an affirmative prefix (e.g., ``Sure''), which does not guarantee sustained harmful output -- the model may begin with apparent agreement yet revert to safety-aligned refusal within a few decoding steps. We address this reliability gap by shifting the backdoor objective from surface tokens to internal representations. We extract a steering vector that captures the difference between compliant and refusal behaviors, and compile it into a persistent weight modification that activates only when the trigger is present. To preserve stealthiness and benign utility, we impose a null-space constraint so that the injected edit remains dormant on clean inputs. The method is efficient, requiring only a small set of examples and admitting a closed-form solution. Across multiple safety-aligned LLMs and jailbreak benchmarks, our method achieves high triggered attack success while maintaining non-triggered safety and general utility.

Abstract

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