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#backdoor-attacks News & Analysis

30 articles tagged with #backdoor-attacks. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

30 articles
AINeutralarXiv – CS AI · May 126/10
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Beyond the False Trade-off: Adaptive EWC for Stealthy and Generalizable T2I Backdoors

Researchers propose Cosine-Aware Adaptive Elastic Weight Consolidation (EWC) to improve text-to-image model backdoor attacks while maintaining model fidelity and generalization. The method addresses a fundamental trade-off between attack success and output quality by dynamically adjusting regularization weights based on semantic utility, achieving stronger performance on both in-domain and out-of-domain datasets compared to existing approaches.

AIBearisharXiv – CS AI · May 46/10
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BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron

Researchers have developed BadSNN, a novel backdoor attack method targeting Spiking Neural Networks by exploiting hyperparameter variations in spiking neurons. The attack demonstrates superior performance compared to existing backdoor methods and shows resistance to current mitigation techniques, raising security concerns for SNNs used in edge computing and neuromorphic applications.

AINeutralarXiv – CS AI · Apr 146/10
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Critical-CoT: A Robust Defense Framework against Reasoning-Level Backdoor Attacks in Large Language Models

Researchers introduce Critical-CoT, a defense framework that protects large language models against reasoning-level backdoor attacks by fine-tuning models to develop critical thinking behaviors. Unlike token-level backdoors, these attacks inject malicious reasoning steps into chain-of-thought processes, making them harder to detect; the proposed defense demonstrates strong robustness across multiple LLMs and datasets.

AIBullisharXiv – CS AI · Mar 37/108
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DualSentinel: A Lightweight Framework for Detecting Targeted Attacks in Black-box LLM via Dual Entropy Lull Pattern

Researchers introduce DualSentinel, a lightweight framework for detecting targeted attacks on Large Language Models by identifying 'Entropy Lull' patterns - periods of abnormally low token probability entropy that indicate when LLMs are being coercively controlled. The system uses dual-check verification to accurately detect backdoor and prompt injection attacks with near-zero false positives while maintaining minimal computational overhead.

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