Papers with adaptive attack

3 papers
Sequential Randomized Smoothing for Adversarially Robust Speech Recognition (2021.emnlp-main)

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Challenge: Existing, naive defenses against adversarial attacks are lagging . a new paper aims to break these defenses with adaptive noise ensembling .
Approach: They propose a randomized smoothing paradigm that can be used to break adversarial attacks . they use speech enhancement methods and a novel use for ASR output ensembling methods .
Outcome: The proposed model is robust to all attacks that use inaudible noise and can only be broken with very high distortion.
Self-Reflection Improves Safety of Large Reasoning Models (2026.findings-acl)

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Challenge: Existing safety alignment methods are shallow and do not address deeper risks and attacks in reasoning processes.
Approach: They propose a technique that introduces a special Self-Reflection token to enable LRMs to perform self-reflection during generation and recover from harmful outputs.
Outcome: The proposed approach outperforms the baseline model in terms of safety and helpfulness, and significantly improves model safety without adversarial training.
Resolving the Security-Auditability Dilemma with Auditable Latent Chain-of-Thought Alignment (2026.acl-long)

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Challenge: Extensive experiments show that ALCA reduces the success rate of adaptive jailbreak attacks by over 40% compared to strong baselines, while preserving performance.
Approach: They propose a framework that decouples internal reasoning from external output and allows the model to reconstruct its latent reasoning into human-readable text for supervision under specific guidance.
Outcome: The proposed framework reduces the success rate of adaptive jailbreak attacks by over 40% compared to baselines while preserving performance.

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