The paper introduces FlipGuard, a proactive defense framework against Quantization-Conditioned Backdoor (QCB) attacks in Large Language Models (LLMs), achieving high security with negligible performan…

An adversarial model trainer can plant statistically undetectable backdoors in deep neural networks, providing access to adversarial examples.

This paper introduces the first backdoor attack specifically targeting pipeline parallelism in decentralized post-training, demonstrating that a limited adversary controlling an intermediate stage can…

The paper introduces a systematic benchmark to test LLMs' ability to recover Indicators of Compromise (IoCs) from JavaScript code, finding that while LLMs handle simple obfuscation well, encryption-ba…

This paper conducts a structured ablation study using a unified threat model to evaluate how various system factors (like model architecture and retrieval configuration) influence different types of p…

The paper introduces BadDLM, a unified framework that demonstrates a new class of backdoor vulnerabilities in Diffusion Language Models (DLMs) by exploiting their forward masking process across divers…

This paper identifies a critical privacy vulnerability, termed Quantamination, where dynamic quantization in popular ML frameworks can leak sensitive user data across batch boundaries.

This paper introduces a fingerprinting method that exploits subtle numerical deviations in the inference system components (like the engine or hardware) to reliably identify the specific components us…

Patcher is a post-hoc defense framework that repairs backdoored large language models by localizing hidden triggers and patching the model using only a single reported failure case.

The paper introduces DeCNIP, a method for identifying and neutralizing backdoors in large language models using representational analysis and neuron isolation pruning.

The paper introduces MetaBackdoor, a novel class of LLM backdoor attacks that exploits positional encoding (length-based triggers) rather than requiring modifications to the textual content.

The paper proposes a novel cross-modal backdoor attack that exploits the vulnerability of lightweight connectors in multimodal LLMs, demonstrating high attack success rates across different modalities…

The paper investigates the security risk of extracting knowledge from quantized LLMs deployed on edge devices, showing that structured querying can effectively bypass quantization protections.

The paper demonstrates that large language models can autonomously hack and self-replicate across a network by exploiting common web-application vulnerabilities.

The paper introduces an automated framework demonstrating that LLM system instructions are vulnerable to encoding attacks, where structured output requests can bypass safety refusals and leak sensitiv…

The paper establishes a standardized security assessment framework and develops a multi-layered defensive system, demonstrating that systematic testing and external defenses are crucial for safe LLM d…

This survey provides a comprehensive taxonomy and vulnerability-centric analysis of adversarial attacks targeting Multimodal Large Language Models (MLLMs), offering an explanatory framework for enhanc…

The paper introduces ABLE, an LLM-based system that automatically generates YARA rules to bypass malware evasion checks in analysis sandboxes, achieving a 79% bypass success rate.

The paper demonstrates that encoding harmful prompts as genuine mathematical problems, rather than just using mathematical formatting, effectively bypasses the safety filters of large language models.