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July 26, 2026Computational LinguisticsOpen Access

Strategic Deflection: Defending LLMs from Logit Manipulation

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Authors

YRYassine RachidyJRJihad R’baitiYHYoussef Hmamouche

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Overview

Randomized trial shows that Strategic Deflection reduces attack success rates in LLMs, indicating improved security measures.

Key Points

  • The study aims to enhance the security of large language models against advanced logit manipulation attacks.
  • Introduces Strategic Deflection to alter model responses to malicious prompts.
  • Conduct experiments to evaluate the effectiveness of SDeflection in reducing attack success rates.
  • Maintains model performance on non-malicious queries during evaluations.
  • SDeflection significantly lowers attack success rate compared to traditional refusal methods.
  • Model performance on benign queries remains stable while implementing SDeflection.
  • Demonstrates a critical shift in defensive strategies for protecting LLMs.

Cite This Study

Rachidy et al. (2026) studied this question.

synapsesocial.com/papers/6a65a3a3d3aea3239cd76a03https://doi.org/10.1162/coli.a.645
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Also Consider

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  1. 1Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks2024 · 3 citations
  2. 2InDe-LLM: Defending against Jailbreak Attacks in LLM-Powered Systems via Intention Disentangling2026
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  4. 4Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing2024
  5. 5Protecting Your LLMs with Information Bottleneck2024