ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation

Multimodal large language models (MLLMs) are increasingly used to automate

Memory Poisoning Attack and Defense on Memory Based LLM-Agents

memory and influence future responses. Recent work demonstrated that the MINJA (Memory Injection Attack) achieves over 95 % injection success rate and 70 % attack success rate under idealized conditions. However

TFL: Targeted Bit-Flip Attack on Large Language Model

safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks. Recent studies have shown that bit-flip attacks (BFAs), which exploit computer main memory

Fragile Thoughts: How Large Language Models Handle Chain-of-Thought Perturbations

Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the robustness of this approach to corruptions in intermediate reasoning

OI-Bench: An Option Injection Benchmark for Evaluating LLM Susceptibility to Directive Interference

signals such as social cues, framing, and instructions. In this work, we introduce option injection, a benchmarking approach that augments the multiple-choice question answering (MCQA) interface with an additional

Langroid has WAF Bypass Leading to RCE in TableChatAgent

Whisper Leak: a side-channel attack on Large Language Models

paramount. This paper introduces Whisper Leak, a side-channel attack that infers user prompt topics from encrypted LLM traffic by analyzing packet size and timing patterns in streaming responses. Despite

Has the Two-Decade-Old Prophecy Come True? Artificial Bad Intelligence Triggered by Merely a Single-Bit Flip in Large Language Models

Recently, Bit-Flip Attack (BFA) has garnered widespread attention for