AIBearisharXiv – CS AI · Jun 237/10
🧠Researchers demonstrate that prompt compression—a technique used to reduce LLM latency and costs—creates a new security vulnerability when processing mixed trusted and untrusted inputs. By strategically perturbing untrusted data before compression, attackers can force compressors to discard critical task information or safety guardrails, achieving 71% attack success rates through a black-box method called COMA.
AIBearisharXiv – CS AI · Jun 87/10
🧠Researchers have discovered that large language models generate code with recurring, predictable vulnerabilities that can be exploited through a black-box attack called FSTab. The technique achieves up to 94% attack success by identifying patterns in LLM-generated software without requiring access to source code, raising critical security concerns for production systems relying on AI code generation.
🧠 GPT-5🧠 Claude🧠 Gemini
AIBearisharXiv – CS AI · Jun 57/10
🧠Researchers have developed a new adversarial attack method against automatic speech recognition systems that operates in feature space rather than directly on audio waveforms, achieving significantly higher transfer rates to black-box ASR models and bypassing existing defenses. The attack uses self-supervised learning representations and vocoders to reconstruct adversarial signals, revealing critical vulnerabilities in current ASR robustness evaluation protocols.
AIBearisharXiv – CS AI · Jun 27/10
🧠Researchers introduce DiscourseFlip, a novel attack method against Retrieval-Augmented Generation (RAG) systems that manipulates opinions across multiple related queries by poisoning retrieval content at the discourse level. Unlike previous attacks targeting individual queries, this coordinated approach induces broader opinion shifts while evading detection, and existing defenses prove ineffective against it.
AIBearisharXiv – CS AI · May 277/10
🧠Researchers have demonstrated a new adversarial attack framework called Multi-Modal Adversarial Synergy (MMAS) that can compromise Vision-Language Models through simultaneous perturbations of both images and text using only black-box queries. This work exposes significant security vulnerabilities in LVLMs that could threaten real-world applications like autonomous driving and content moderation systems.
AIBearisharXiv – CS AI · May 277/10
🧠Researchers have developed SD-MIA, a black-box membership inference attack that can detect whether specific images were used in training diffusion-based image generation models by analyzing how the model denoise images and perturbed text instructions. This technique outperforms existing methods without requiring access to internal model features, raising significant privacy and copyright concerns for AI developers and users.
AIBearisharXiv – CS AI · Mar 57/10
🧠Researchers have developed Image-based Prompt Injection (IPI), a black-box attack that embeds adversarial instructions into natural images to manipulate multimodal AI models. Testing on GPT-4-turbo achieved up to 64% attack success rate, demonstrating a significant security vulnerability in vision-language AI systems.
🧠 GPT-4
AIBearisharXiv – CS AI · Feb 277/107
🧠Researchers developed CC-BOS, a framework that uses classical Chinese text to conduct more effective jailbreak attacks on Large Language Models. The method exploits the conciseness and obscurity of classical Chinese to bypass safety constraints, using bio-inspired optimization techniques to automatically generate adversarial prompts.
AIBearisharXiv – CS AI · Mar 37/106
🧠Researchers developed AdvBandit, a new black-box adversarial attack method that can exploit neural contextual bandits by poisoning context data without requiring access to internal model parameters. The attack uses bandit theory and inverse reinforcement learning to adaptively learn victim policies and optimize perturbations, achieving higher victim regret than existing methods.
