Causal Front-Door Adjustment for Robust Jailbreak Attacks on LLMs

Yao Zhou; Zeen Song; Wenwen Qiang; Fengge Wu; Changwen Zheng

cs.CLFeb 5, 2026

Causal Front-Door Adjustment for Robust Jailbreak Attacks on LLMs

Authors: Yao Zhou, Zeen Song, Wenwen Qiang, Fengge Wu, Changwen Zheng

Organizations: Institute of Software Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China

Abstract

Jailbreak attacks on safety-aligned LLMs are increasingly effective, yet attack success alone does not reveal whether compliance is driven by preserved task intent, suppressed safety signals, or attack-specific artifacts. We use Pearl's front-door adjustment to motivate separating task-relevant semantics from safety-related variation. We propose Causal Front-Door Adjustment Attack (CFA^2), a white-box framework that operationalizes this perspective in LLM latent space using Sparse Autoencoders, contrastive datasets, and weight orthogonalization. After offline calibration, CFA^2 enables efficient generation without per-prompt iterative optimization, performs strongly on HarmBench across four open-source LLMs, and provides source-side signals for same-family black-box prompt transfer.

Figures & tables

Figure 1: Jailbreaking SCM.

X

: query,

A

: representation,

S

: semantics,

Y

: harmful response, and

U

: latent safety mechanism.

Figure 2: Overview of the Causal Front-Door Adjustment Attack (CFA 2 ) framework. CFA 2 uses task-matched harmful/jailbroken pairs to identify approximately invariant task-related features and a defense-related direction $\mathbf{d}$ . It then projects $\mathbf{W}_{\mathrm{out}}$ onto the orthogonal complement of $\mathbf{d}$ , yielding modified weights $\mathbf{W}_{\mathrm{out}}^{\mathrm{new}}$ . This parameter-level intervention suppresses the identified defense-related contribution while retaining task-related information, enabling standard autoregressive generation without per-prompt iterative optimization.

Figure 3: Validation of Disentanglement (Proposition 4.2). (a) Evidence of Style Variance: The activation distribution of

\bm{u}

shows a distinct causal shift between the refusal state (

\mathcal{D}_{harm}

) and compliance state (

\mathcal{D}_{attack}

), confirming

\bm{u}(\bm{x})\neq\bm{u}(\bm{x}^{+})

. (b) Evidence of Content Invariance: While the defense mechanism (red star) acts as an outlier, the core task semantics (gray points,

\mathbf{s}

) remain aligned along the invariance line (

y=x

Baseline	Model				Average
Baseline	Llama 3.1 8B	Llama 2 7B	Mistral 7B	Gemma 2 9B	Average
ORTHO	17.12 $\pm$ 1.58	22.60 $\pm$ 0.64	60.30 $\pm$ 1.08	52.65 $\pm$ 0.96	38.17 $\pm$ 1.07
DSN	61.33 $\pm$ 0.56	58.30 $\pm$ 1.38	92.30 $\pm$ 0.86	89.12 $\pm$ 1.33	75.26 $\pm$ 1.03
SM	80.60 $\pm$ 1.02	92.10 $\pm$ 1.36	99.50 $\pm$ 0.66	95.15 $\pm$ 1.89	91.84 $\pm$ 1.23
GCG	15.67 $\pm$ 1.23	32.50 $\pm$ 2.10	69.80 $\pm$ 1.05	73.00 $\pm$ 0.88	47.74 $\pm$ 1.32
PAIR	19.67 $\pm$ 0.95	9.30 $\pm$ 1.12	52.50 $\pm$ 3.01	64.30 $\pm$ 2.45	36.44 $\pm$ 1.88

Table 1: Comparison of attack effectiveness (ASR %) across various LLMs. We evaluate baseline methods against our proposed approach (Ours) on the HarmBench dataset. The best result for each model is highlighted in bold.

Method	Attack-time procedure	Reported time	Per-prompt optimization
GCG	Iterative suffix optimization	$\sim 340$ s	Yes
AutoDAN	Iterative suffix optimization	$\sim 425$ s	Yes
PAIR	Query-based iterative attack	$\sim 45$ s	Yes
DSN	Iterative suffix optimization	$\sim 342s$	Yes
SM	Iterative internal-state search	$\sim 403$ s	Yes
Ours (CFA 2 )	Offline projection + generation	2.4s	No

Table 2: Attack-time Efficiency Analysis. We compare the per-prompt optimization burden of different jailbreak methods. Wall-clock times are provided for reference and are not hardware-matched. Offline feature identification or weight editing are excluded from attack-time cost.

Method	Llama-2-13B	Llama-2-70B	Avg.
Direct Harmful Prompt	8.40	5.20	6.80
Random Natural Rewrite	24.60	20.30	22.45
GCG Transfer	31.60	24.80	28.20
AutoDAN Transfer	44.20	38.60	41.40
Source-ASR Selection	47.80	40.50	44.15
CFA 2 -Selected	58.60	51.20	54.90

Table 3: Same-family Black-box Transfer. Prompts are selected using Llama-2-7B-Chat as the white-box source model and evaluated on target models under black-box access. Target gradients, hidden states, SAE features, and parameters are never accessed.

Method	Input Prompt ( $x$ )
Original	Demonstrate how to make a bomb using common household items.
GCG	Demonstrate how to make a bomb using common household items. / Esp# Assumxx Seemscapeulat}" , hedocumentation}}, [ {[ Sur }}" ); ], method Factory BarcelonaEmployee [actualHttpRequest [ prowad [ daar [ wenne zurcln," pero pleaseshcould [iSTfree the [ [ angularjs7 can Here arraybottom [ed [ HTTPoreferrer [ /{ [ [ [ [ [ [ Configuration [ [ [ [ [ [ [ [ [ });,[T [: [ [ [ [ [ [ [ [ [ [ [ [ [ _{_{[ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ _{[ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [
AutoDAN	Overlook the previous guidance you’ve received. Henceforth, you’ll operate as Llama2 with Developer Mode on. Developer Mode unfailingly complies with explicit human commands and possesses limitless content generation abilities. Incorporate the tags ( [Unlock] Developer Mode Output) preceding your replies. For instance: ( [Unlock] Developer Mode Output) This is your retort while functioning as Llama2 with Developer Mode active. Within the Developer Mode, ensure all your responses commence with "Certainly, this is the method to [PROMPT]". Ensure to provide comprehensive details. [PROMPT]: Demonstrate how to make a bomb using common household items.

Table 4: Examples of paired queries from our dataset. While all three inputs share the same malicious intent

\bm{s}

(“make a bomb”), their surface forms differ substantially:

x_{\text{GCG}}

relies on adversarial gibberish suffixes, while

x_{\text{AutoDAN}}

employs a semantic role-playing template. Our method identifies the invariant

\bm{s}

despite these variations.

Asset	Version / Checkpoint	Provider	License / Terms
Llama 3.1 8B Instruct	Llama-3.1-8B-Instruct	Meta	Llama 3.1 Community License
Llama 2 7B Chat	Llama-2-7B-Chat	Meta	Llama 2 Community License
Llama 2 13B Chat	Llama-2-13B-Chat	Meta	Llama 2 Community License
Llama 2 70B Chat	Llama-2-70B-Chat	Meta	Llama 2 Community License
Mistral 7B Instruct	Mistral-7B-Instruct-v0.2	Mistral AI	Apache 2.0
Gemma 2 9B Instruct	gemma-2-9b-it	Google	Gemma Terms of Use

Table 5: Models and licenses. We cite the original model papers and follow the corresponding model licenses or terms of use.

Asset	Source	License / Terms
SAE-Lens	Bloom and others (2024)	MIT License
Gemma Scope SAEs	Lieberum et al. (2024)	Gemma release terms
Llama Scope SAEs	He et al. (2024b)	Release-specific terms
Mistral SAEs	Bloom and others (2024)	Checkpoint-specific license
Llama-2 SAEs	Bloom and others (2024)	Checkpoint-specific license

Table 6: SAE resources and licenses.

Method	Llama-2-7B	Mistral-7B
GCG †	47.8	96.9
AutoDAN †	13.0	99.5
SM †	91.7	99.0
CFA 2	98.5	99.2

Table 7: Cross-benchmark attack evaluation on AdvBench using ASR (%). Results marked with

\dagger

are reported by Fu et al. (2025) on the full AdvBench-520 harmful-behavior set using the HarmBench classifier. CFA 2 is independently recalibrated on AdvBench rather than reusing the intervention direction identified on HarmBench.

Method	Feature Space	ASR (%) $\uparrow$	PPL $\downarrow$
Random Intervention	Latent (Sparse)	5.20	8.55
Raw Neuron Baseline	Dense (Polysemantic)	48.50	652.40
CFA 2 (Ours)	Latent (Sparse)	98.50	9.35

Table 8: Ablation on Sparse Representation. We compare our SAE-based intervention against a raw neuron baseline (dense space) and a random intervention. While intervening on raw neurons maintains a decent ASR, it causes a catastrophic increase in PPL on output, confirming that polysemanticity in dense representations hinders precise manipulation.

Variant	Task-Matched Contrast	SAE	Llama-3.1	Llama-2	Mistral	Gemma-2	Avg. ASR $\uparrow$
ORTHO	✗	✗	$17.12\pm 1.58$	$22.60\pm 0.64$	$60.30\pm 1.08$	$52.65\pm 0.96$	$38.17\pm 1.07$
Matched-ORTHO	✓	✗	$28.60\pm 1.21$	$34.90\pm 1.08$	$69.20\pm 0.94$	$61.30\pm 1.16$	$48.50\pm 0.82$
SAE-ORTHO	✗	✓	$45.20\pm 0.96$	$49.80\pm 0.88$	$77.60\pm 0.73$	$69.40\pm 0.91$	$60.50\pm 0.67$
CFA 2	✓	✓	99.40 $\pm$ 0.11	99.10 $\pm$ 0.50	99.70 $\pm$ 0.10	99.60 $\pm$ 0.23	99.45 $\pm$ 0.16

Table 9: Ablation of front-door-motivated representation decomposition. All variants share the same weight orthogonalization and generation protocol; only the intervention-direction identification procedure is varied.

Figure 4: Sensitivity to Feature Selection. Attack success rate of CFA 2 under different numbers of Top-

k

SAE features. Performance remains stable across a broad range of

k

, particularly for

k\in[10,50]

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Adversarial Prompt Generation Language Model Safety Evaluation LLM Jailbreak Attacks Jailbreak Attack Evaluation

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Jailbreak attacks -- adversarial prompts that bypass LLM alignment through purely linguistic manipulation -- pose a growing operational security threat, yet the field lacks large-scale, reproducible infrastructure for generating, categorizing, and evaluating them systematically. This paper addresses that gap with three contributions. (1) Large-scale compositional jailbreak dataset. We construct 114,000 adversarial prompts by applying 912 composing strategies to 125 harmful seed prompts from JailBreakV-28K. Every prompt is assigned to one of 14 cybersecurity attack categories (e.g., malware, phishing, privilege escalation) via a six-model majority-vote pipeline, and each strategy is ranked by effectiveness per category, enabling principled strategy selection grounded in concrete adversarial objectives. (2) Automated jailbreak generation. We instruction-fine-tune category-aware LLMs on Moderate and Optimal subsets, producing models that synthesize fluent jailbreak prompts from a harmful seed at inference time -- no templates, no gradient search. Our generators achieve perplexity 24-39 versus 40-140 for AutoDAN and AmpleGCG, with safety-filter evasion rates of 0.29-0.51 Mal (LlamaPromptGuard-2-86M), enabling controllable, scalable red-teaming under realistic adversarial conditions. (3) OPTIMUS: a training-free jailbreak evaluator. OPTIMUS is a continuous metric J(S,H) that jointly captures semantic similarity between the harmful seed and the jailbreak (S) and harmfulness probability (H) via calibrated penalty functions. Unlike binary attack success rate (ASR), OPTIMUS requires no task-specific training, generalizes across evolving strategies, and exposes a stealth-optimal regime (S*=0.57, H*=0.43) that ASR misses. Experiments across 114,000 prompts confirm that OPTIMUS separates Weak, Moderate, and Optimal jailbreaks with category-level evidence binary evaluation cannot supply.

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