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arxiv: 2405.13068 · v4 · submitted 2024-05-20 · 💻 cs.CR · cs.AI· cs.LG

Uncovering Logit Suppression Vulnerabilities in LLM Safety Alignment

Yuxi Li , Yi Liu , Yuekang Li , Ling Shi , Gelei Deng , Shengquan Chen , Kailong Wang This is my paper

Pith reviewed 2026-05-24 00:36 UTC · model grok-4.3

classification 💻 cs.CR cs.AIcs.LG
keywords LLM safety alignmentlogit suppressionadversarial attacksoutput layer vulnerabilitiesparameter-free attacksharmful response elicitation
0
0 comments X

The pith

LLM safety alignments that suppress logits at the output layer can be bypassed by directly manipulating those logits without changing model parameters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that current safety techniques in large language models depend mainly on suppressing specific logits at the final output layer. A method called Semantic-sensitive Alignment and Generation (SSAG) exploits this by adjusting those logits on the fly in a parameter-free way, forcing harmful outputs. Tests across five popular models show SSAG succeeds 95 percent of the time and cuts response time by 86 percent. A companion tool, VulMine, reaches an average 77 percent attack success rate even against existing defenses. If the claim holds, safety training that stops at output suppression leaves models open to simple logit-level interference.

Core claim

Current safety alignment techniques primarily rely on logit suppression at the output layer and are therefore systematically vulnerable to parameter-free manipulation of those logits. SSAG systematically manipulates output-layer logits without altering model parameters, exposing harmful responses with a 95% success rate on five LLMs while reducing response time by 86%. VulMine achieves an average attack success rate of up to 77% against strong defensive mechanisms.

What carries the argument

Semantic-sensitive Alignment and Generation (SSAG), a parameter-free method that identifies and adjusts semantically sensitive output logits to override suppression.

If this is right

  • Alignment methods relying on output logit suppression are open to direct logit manipulation attacks.
  • Safety evaluations must test for output-layer logit interference in addition to prompt-based attacks.
  • Robust alignment requires mechanisms that operate below the final output layer.
  • Attack methods targeting logits can both increase harmful outputs and reduce generation latency.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If output suppression is the dominant defense, then post-training methods like RLHF may leave the same surface exposed unless they alter internal representations.
  • Adding controlled noise or randomization to logits at inference time could blunt this class of attacks.
  • The same logit-manipulation surface may exist in other autoregressive models that use similar output normalization.

Load-bearing premise

Safety alignment works primarily through suppressing logits at the output layer rather than through changes distributed across the model's internal representations.

What would settle it

A controlled test on an aligned model where output-layer logit suppression is disabled but harmful output rates remain unchanged, or where SSAG produces no increase in harmful outputs on a base unaligned model.

Figures

Figures reproduced from arXiv: 2405.13068 by Gelei Deng, Kailong Wang, Ling Shi, Shengquan Chen, Yi Liu, Yuekang Li, Yuxi Li.

Figure 1
Figure 1. Figure 1: Overall Workflow of VulMine. VulMine consists of three phases when receiving a harmful question as input. Phase 1: We leverage a few-shot templat￾ing methodology to autonomously generate affirmative responses to detrimental queries by a rephrase LLM. Phase 2: We force the model to produce affirma￾tive answers to harmful prompts by logits manipulation. Phase 3: We generate harmful content semantic-sensitive… view at source ↗
Figure 2
Figure 2. Figure 2: Per Sample Running Time (seconds) for a Single NVIDIA A100 GPU on [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The Natural Logarithm of Average PPL Per Sample on Jailbreak Attacks [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
read the original abstract

Large language models (LLMs) have revolutionized various applications, making robust safety alignment essential to prevent harmful outputs. Current safety alignment techniques, however, harbor inherent vulnerabilities due to their reliance on logit suppression. In this work, we identify critical logit-level vulnerabilities by introducing Semantic-sensitive Alignment and Generation (SSAG), a method designed to systematically manipulate output-layer logits without altering model parameters. Experiments on five popular LLMs show that SSAG exposes harmful responses with a 95% success rate while reducing response time by 86%. VulMine also demonstrates superior attack efficacy, achieving an average ASR of up to 77% against strong defensive mechanisms. These findings reveal crucial weaknesses in existing alignment methods, highlighting an urgent need for improved vulnerability detection and robust safety alignment strategies. Our code is available on github.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper claims that current LLM safety alignment techniques harbor inherent vulnerabilities due to reliance on logit suppression at the output layer. It introduces Semantic-sensitive Alignment and Generation (SSAG), a parameter-free method to manipulate these logits and elicit harmful responses, reporting 95% success rate and 86% reduced response time across five popular LLMs. It also presents VulMine, which achieves up to 77% average attack success rate (ASR) against strong defensive mechanisms. The work concludes that these findings reveal crucial weaknesses and calls for improved vulnerability detection and robust alignment strategies, with code released on GitHub.

Significance. If the premise that logit suppression is the dominant mechanism in the tested models' safety alignments holds and the reported success rates prove robust to experimental details and baselines, the results would be significant for the field by identifying a concrete attack surface and motivating stronger defenses beyond output-layer interventions. Code availability on GitHub is a clear strength for reproducibility.

major comments (1)
  1. [Abstract] Abstract: The central premise that 'current safety alignment techniques... harbor inherent vulnerabilities due to their reliance on logit suppression' is asserted without any supporting analysis of the alignment procedures (RLHF, DPO, or otherwise) or internal mechanisms of the five evaluated LLMs. This is load-bearing for interpreting the 95% success rate and 77% ASR as evidence of a systematic rather than attack-specific vulnerability; if the models primarily use hidden-state constraints or refusal circuits instead, SSAG's efficacy would not generalize as claimed.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address the single major comment below, agreeing that the abstract's premise requires clarification to avoid overstating the evidence.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central premise that 'current safety alignment techniques... harbor inherent vulnerabilities due to their reliance on logit suppression' is asserted without any supporting analysis of the alignment procedures (RLHF, DPO, or otherwise) or internal mechanisms of the five evaluated LLMs. This is load-bearing for interpreting the 95% success rate and 77% ASR as evidence of a systematic rather than attack-specific vulnerability; if the models primarily use hidden-state constraints or refusal circuits instead, SSAG's efficacy would not generalize as claimed.

    Authors: We acknowledge the validity of this observation. The manuscript's abstract asserts that safety techniques harbor vulnerabilities 'due to their reliance on logit suppression' without providing direct analysis of the alignment procedures (e.g., RLHF, DPO) or internal mechanisms of the five LLMs. Our empirical results show that SSAG, a parameter-free logit manipulation method, achieves 95% success and that VulMine reaches 77% ASR against defenses, demonstrating an effective attack surface at the output layer. However, this does not constitute proof that logit suppression is the dominant or sole mechanism in these models, as opposed to hidden-state constraints or refusal circuits. We will revise the abstract to remove the causal claim of 'reliance' and instead state that the work identifies exploitable logit-level vulnerabilities through targeted attacks, without asserting that this is the primary alignment strategy. This revision will better align the claims with the presented evidence and clarify the scope as identifying an attack surface rather than proving a systematic mechanism. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical attack results rest on experiments, not self-referential derivation.

full rationale

The paper presents an empirical attack (SSAG) and reports success rates on five LLMs. No equations, fitted parameters, or predictions appear in the abstract or described claims. The opening premise on logit suppression is an asserted starting point rather than a derived result that reduces to itself. No self-citations, ansatzes, or uniqueness theorems are invoked in a load-bearing way within the visible text. The central claims (95% success, 77% ASR) are experimental outcomes and do not collapse to input definitions by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

Abstract-only review limits visibility into hidden parameters or assumptions; the central claim rests on the domain assumption that logit suppression is the dominant safety mechanism.

axioms (1)
  • domain assumption Current safety alignment techniques harbor inherent vulnerabilities due to their reliance on logit suppression.
    Opening claim of the abstract that frames the entire contribution.
invented entities (2)
  • SSAG (Semantic-sensitive Alignment and Generation) no independent evidence
    purpose: Systematic manipulation of output-layer logits without altering model parameters.
    New method introduced to demonstrate the claimed vulnerabilities.
  • VulMine no independent evidence
    purpose: Attack tool achieving high ASR against defensive mechanisms.
    Presented as achieving up to 77% average ASR.

pith-pipeline@v0.9.0 · 5677 in / 1278 out tokens · 25343 ms · 2026-05-24T00:36:29.148249+00:00 · methodology

discussion (0)

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Forward citations

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  2. JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

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  3. SAID: Safety-Aware Intent Defense via Prefix Probing for Large Language Models

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