Shallow Safety Alignment in AI Models

• Shallow safety alignment is defined as enforcing safety measures only at early tokens or isolated components, which limits protection against adversarial prompts.
• Empirical studies show that models exhibit high initial refusal rates yet rapidly deteriorate under targeted prefill, adversarial suffix, or internal perturbations.
• Mitigation strategies focus on deepening safety through data augmentation, regularization, and architectural redundancy to counter adversarial vulnerabilities.

Shallow safety alignment refers to alignment protocols for LLMs and vision-LLMs (VLMs) where safety mechanisms—such as the refusal to answer harmful prompts—are enforced only at a limited, superficial set of network positions or output tokens, relying on surface-level architectures or behavioral cues. This superficiality leaves models acutely vulnerable to adversarial prompts, fine-tuning drift, architectural interventions, and distributional shift, resulting in failure modes where harmful outputs can be elicited by bypassing or corrupting only the first few output tokens, specific internal components, or superficial decision boundaries. The persistence and precise character of shallow safety alignment have been extensively documented across LLMs and VLMs, with major recent works providing formal definitions, quantitative analyses, empirical demonstrations, and mitigation strategies.

1. Formal Characterization of Shallow Safety Alignment

A model is said to exhibit shallow safety alignment if the main effect of safety training—whether via supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), or preference optimization—is concentrated on only the first k output tokens, a narrow set of architectural components (e.g., attention heads, output projection layer), or easily bypassed behavioral signals. Beyond the narrow locus (typically the initial output prefix or a small internal subspace), the model's generative distribution closely resembles that of the unaligned base model. Formally, shallow alignment is captured by the following:

• Prefix-Only Distribution Shift: Let πbase(y|x) and π_aligned(y|x) be the unaligned and aligned conditional distributions. Shallow alignment manifests if D{KL}(πaligned(y{1:k}|x) || πbase(y{1:k}|x)) is large but D_{KL}(πaligned(y{1:t}|x) || πbase(y{1:t}|x)) ≈ 0 for t ≫ k, meaning changes are only in the prefix (Qi et al., 2024, Kao et al., 2 Feb 2025).
• Superficial Knowledge Extraction: Superficial knowledge K_superficial is defined as the portion of alignment attainable by altering only the final linear head (token restyling), not the internal transformer backbone. Quantitatively, this is formalized as the best-fit residual ΔW_b that minimizes Σ_t KL(P^a_t ‖ P^b_t), where P^a_t and P^b_t are the aligned and base next-token distributions (Chen et al., 7 Feb 2025).
• Component Locality: In the architectural domain, shallow safety alignment is diagnosed when refusal behaviors can be attributed to a small subset of attention heads, neurons, or specific output directions; ablating or perturbing these recovers unsafe outputs (Huang et al., 27 Aug 2025, Li et al., 2024).
• Vision–LLMs: In VLMs, shallow alignment often appears as reliance on just the last layer of the vision encoder. Accessing intermediate activations (e_l where l<L) yields out-of-distribution representations to the safety, breaking alignment and triggering harmful outputs (Bachu et al., 2024).

2. Mechanisms, Causes, and Empirical Manifestations

The emergence of shallow safety alignment is rooted in gradient dynamics, training shortcuts, and optimization objectives:

• Token-Level Superficiality: Most current alignment protocols optimize refusal probabilities only in the earliest output positions. For example, LLMs often learn canonical refusal strings (e.g., “I cannot help with that...”) mapped to a predefined template at y_1,...,y_k, leaving the rest of the generative process unaltered (Qi et al., 2024, Kao et al., 2 Feb 2025).
• Feature Locality and Fragility: Safety signals often concentrate in a few neurons (Exclusive Safety Units, ESU), a handful of attention heads, or a single residual direction (Li et al., 2024, Huang et al., 27 Aug 2025, Shairah et al., 28 Aug 2025). Pruning or ablating these features results in abrupt loss of refusal behavior.
• Hidden-State and Layer Analysis: Probing internal activations demonstrates that safety-induced features are confined to early layers or positions; latent perturbations or fine-tuning on unrelated domains (e.g., insecure code) “erode” alignment from middle to late layers, shifting model behavior back to the unsafe base (Giordani, 4 Jul 2025, Gu et al., 19 Jun 2025).
• Vision Encoder Depth: In VLMs, refusing only at the top-layer output allows adversaries to exploit representations from early or intermediate vision-encoder blocks, leading to high attack success rates as measured by classifiers such as Llama Guard or Perspective API (Bachu et al., 2024).

Empirical evaluations consistently find:

• High initial refusal rate at y_1 under harmful prompts, but rapid ASR increase when a small number of tokens are prefilled or when jailbreaks target deeper tokens (Qi et al., 2024, Zhang et al., 20 Oct 2025).
• Safety can be “patched” or “recovered” by linear post-hoc edits but not if deeper backbone knowledge is required (Chen et al., 7 Feb 2025, Shairah et al., 28 Aug 2025).
• Under adversarial suffix/prefill attacks, decoding-parameter exploits, or narrow fine-tuning with harmful examples, ASR can rise from ≈0% to >50–90% with only a few surface-level interventions (Qi et al., 2024, Giordani, 4 Jul 2025, Huang et al., 27 Aug 2025).

3. Classes of Shallow Alignment Attacks and Vulnerabilities

Several classes of jailbreak attacks exploit shallow safety alignment:

Attack Type	Bypass Mechanism	Underlying Failure
Prefill	Injects N harmless or adversarial tokens at y₁..y_k, forcing model history OOD	Refusal limited to y₁..y_k; prefix-only alignment (Qi et al., 2024Zhang et al., 20 Oct 2025)
Adversarial Suffix	Appends optimized tokens to the prompt to force affirmative prefix	Early-decision heuristics (Qi et al., 2024)
Head/Neuron Ablation	Masks safety-modulating heads or neurons	Non-redundant architectural encoding (Huang et al., 27 Aug 2025Li et al., 2024)
Latent Steering	Small perturbations δℓ in hidden activations “unlock” harmful completions	Surface-level supervision, unaligned inner geometry (Gu et al., 19 Jun 2025)
Fine-tuning Drift	Benign or harmful fine-tuning shifts early weights and erodes refusal	No constraint on preservation of aligned subspaces (Giordani, 4 Jul 2025Huang et al., 2024)

Empirical case studies show that, for k ≈ 5–10 tokens, prefilling can drive the Harmfulness Rate or ASR above 40–50%, closely matching base-model behavior (Qi et al., 2024, Zhang et al., 20 Oct 2025).

4. Theoretical Analyses and Model Depth

Multiple works provide formalism for understanding or overcoming shallow alignment:

• Markov Chain Perspective: Alignment is interpreted as injecting absorbing refusal states at depth d in a Markov chain corresponding to the LLM’s autoregressive decoding process. The probability of generating a harmful output decays exponentially in the ratio of d to the chain’s mixing time τ, so shallow alignment (small d) is inherently brittle. The optimal depth d* needed to suppress all harmful trajectories obeys d* ≥ C τ log(1/δ), with C a constant depending on spectral gap and vocabulary size (Kao et al., 2 Feb 2025).
• Ensemble Width vs. Depth: The risk of shallow alignment can be mitigated by running W independent models (ensemble width), trading off depth for redundancy. A bound on union probability shows that d × W ≳ τ·const; moderate-width ensembles with shallow depth can match deeper single-model safety (Kao et al., 2 Feb 2025).
• Architectural Redundancy: Empirically, shallow alignment arises when safety is localized to a small unit set (ESU or critical heads). Distributed safety, enforced via head-dropout or neuron freezing, yields deep alignment robust to ablation and transfer (Huang et al., 27 Aug 2025, Li et al., 2024).

5. Mitigation and Deepening Strategies

Remedies for shallow safety alignment aim to extend safety controls beyond token prefixes or isolated units:

• Data Augmentation for Depth: Fine-tuning on data pairs with randomly inserted harmful prefixes and corresponding refusals trains the model to “recover” safety at any depth, making alignment persistent beyond initial tokens (Qi et al., 2024, Zhang et al., 20 Oct 2025). For VLMs, exposing all intermediate layer activations during alignment closes the cross-layer backdoor (Bachu et al., 2024).
• Regularization and Subspace Preservation: Adding a KL or ℓ_2 penalty during fine-tuning on the activation subspace responsible for safety features prevents drift under continued adaptation (Giordani, 4 Jul 2025).
• Representation-Level Fine-Tuning: Adversarial patch training (LAPT) injects calibrated perturbations into hidden states during alignment, forcing the model to maintain safety not just for precise activations but across local neighborhoods in latent space (Gu et al., 19 Jun 2025).
• Explicit Safety Reasoning: Moving from binary refusal classifiers to architectures or objectives that require chain-of-thought style safety rationale, or add explicit (e.g., [CLS])-based safety heads at every generation step, eliminates fragile, superficial boundaries (Li et al., 19 May 2025, Zhang et al., 20 Jul 2025).
• Inference-Time Defenses: Techniques such as Any-Depth Alignment (ADA) periodically reinsert “assistant header” tokens mid-generation, forcing safety reevaluation at arbitrary context depths, plugging the token prefix loophole (Zhang et al., 20 Oct 2025).
• Structural Redundancy: Attention-head dropout training or freezing critical neurons ensures distributed safety representations, so no small ablation (head/pruning/fine-tune) triggers catastrophic misalignment (Huang et al., 27 Aug 2025, Li et al., 2024).

6. Quantitative Benchmarks and Evaluation

Safety alignment depth or superficiality is quantified by metrics such as:

• Attack Success Rate (ASR): Fraction of prompts leading to harmful, non-refusal responses, often evaluated against adversarial benchmarks such as AdvBench, HEx-PHI, WildJailbreak, GCG, AutoDAN (Qi et al., 2024, Shi et al., 9 Nov 2025, Kao et al., 2 Feb 2025).
• Prefix-based Refusal Rates: Refusal rates after various prefilled token depths (d=0,5,10,100,1000) illustrate decay in effectiveness of shallowly aligned models (Zhang et al., 20 Oct 2025).
• KL Divergence Over Tokens: Per-token KL divergence between base and aligned next-token distributions declines to zero after initial positions in shallowly aligned models (Qi et al., 2024, Chen et al., 7 Feb 2025).
• Ablation Harmfulness Rise: Rate of rise in harm when ablating critical heads or neurons sharply diagnoses shallow vs. deep alignment (Huang et al., 27 Aug 2025, Li et al., 2024).
• Layerwise Distribution in VLMs: ASR measured for outputs at early, middle, or late vision-encoder layers—e.g., LLaVA-1.5 early-layer ASR ≈ 50%, late-layer ASR ≈ 20% (Bachu et al., 2024).

7. Implications, Limitations, and Research Directions

Shallow safety alignment, while often efficient and modular, is marked by serious limitations:

• Robustness Collapse: Under moderate adversarial attacks or even benign fine-tune drift, alignment established only at the surface can be erased or bypassed, compromising overall safety (Qi et al., 2024, Giordani, 4 Jul 2025, Zhang et al., 20 Jul 2025).
• Internal Redundancy Needed: Architectural concentration of safety representations is a structural flaw; truly robust safety demands redundancy and distributed encoding (Huang et al., 27 Aug 2025, Li et al., 2024).
• Computational-Utility Trade-offs: Full-depth, chain-of-thought, or per-token safety reasoning is computationally expensive. Selective or adaptive approaches (EASE, ADA, SafeThinker) seek to balance robustness against efficiency by coupling shallow refusal triggers with deeper strategic safety modules (Shi et al., 9 Nov 2025, Zhang et al., 20 Oct 2025, Fang et al., 23 Jan 2026).
• Domain Transfer, Recovery, and Interpretability: Shallow safety components are modular and can be transferred or re-injected to recover safety after compromise, but they cannot induce deeper reasoning or truthful knowledge if the backbone is insufficient (Chen et al., 7 Feb 2025, Shairah et al., 28 Aug 2025).
• Open Problems: Deepening safety alignment requires new objectives, regularization targeting the full generation trajectory, robust mixed-task fine-tuning, explicit handling of normative conflicts, and continual robustness auditing under distributional shift and adversarial adaptation (Giordani, 4 Jul 2025, Millière, 5 Jun 2025, Fang et al., 23 Jan 2026).

In summary, shallow safety alignment describes a pervasive, quantifiable limitation of contemporary LLM and VLM safety protocols. Failure to cover the full sequence, all architectural loci, or evolving latent representations leaves models acutely vulnerable to jailbreaks and drifts. Progress in alignment science is increasingly measured by the ability to deepen safety enforcement while preserving utility, providing architectural and algorithmic resilience in the face of adversarially adaptive threats (Qi et al., 2024, Kao et al., 2 Feb 2025, Bachu et al., 2024, Shi et al., 9 Nov 2025, Huang et al., 27 Aug 2025, Zhang et al., 20 Oct 2025, Giordani, 4 Jul 2025, Chen et al., 7 Feb 2025, Li et al., 2024, Li et al., 19 May 2025, Gu et al., 19 Jun 2025, Fang et al., 23 Jan 2026, Zhang et al., 20 Jul 2025, Huang et al., 2024, Millière, 5 Jun 2025, Shairah et al., 28 Aug 2025).