Adaptive_Greedy_Local_Search

自适应贪心局部搜索 AGLS 语义保持的提示劫持：针对自动提示优化的黑盒对抗攻击 ICME 2026 摘要： 大型语言模型（LLMs）日益配备自动提示优化模块，这些模块会重写用户的输入，并在生成最终响应之前明确展示一个"改进后"的提示。尽管这种可见性旨在增强信任和用户控制，但优化器自主选择单一"最佳"候选的过程仍不受约束。攻击者可以劫持原始提示或由自动提示优化生成的提示，进行替代性重写，从而诱发语义漂移并完成劫持优化过程，进而严重影响 LLM...

KidnapRAG

KidnapRAG：一种用于劫持智能体检索增强生成系统推理的黑盒攻击 🎓 论文 | 📄 数据集 | 🚀 快速开始 快速开始 在克隆的 KidnapRAG 仓库的父目录下运行以下命令。 root@kitploit: cd KidnapRAG conda create -n KidnapRAG python=3.10 conda activate KidnapRAG pip install -r requirements.txt 然后，下载语料数据集 📄，并将 ReAct 数据集移动到 /KidnapRAG/ReAct，将 WebThinker 数据集移动到...

MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents

External memory has become a core component of modern web agents, enabling long-horizon reasoning through the retrieval of past experiences. However, this paradigm introduces a critical vulnerability: malicious content injected into memory can be persistently recalled and repeatedly influence age...

MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models

Diffusion large language models dLLMs generate text by iteratively denoising partially masked sequences under bidirectional context, exposing a safety surface distinct from autoregressive LLMs. Because mask tokens are native inputs and tokens are committed by confidence rather than position,...

Babel: Jailbreaking Safety Attention Via Obfuscation Distribution Optimized Sampling

Despite rigorous safety alignment, Large Language Models LLMs remain vulnerable to jailbreak attacks. Existing black-box methods often rely on heuristic templates or exhaustive trials, lacking mechanistic interpretability and query efficiency. In this study, we investigate an intrinsic...

Targeted Adversarial Traffic Generation : Black-Box Approach to Evade Intrusion Detection Systems in IoT Networks

The integration of machine learning ML algorithms into Internet of Things IoT applications has introduced significant advantages alongside vulnerabilities to adversarial attacks, especially within IoT-based intrusion detection systems IDS. While theoretical adversarial attacks have been extensive...

Frequency Bias Matters: Diving into Robust and Generalized Deep Image Forgery Detection

As deep image forgery powered by AI generative models, such as GANs, continues to challenge today's digital world, detecting AI-generated forgeries has become a vital security topic. Generalizability and robustness are two critical concerns of a forgery detector, determining its reliability when...

"To Survive, I Must Defect": Jailbreaking LLMs Via the Game-Theory Scenarios

As LLMs become more common, non-expert users can pose risks, prompting extensive research into jailbreak attacks. However, most existing black-box jailbreak attacks rely on hand-crafted heuristics or narrow search spaces, which limit scalability. Compared with prior attacks, we propose Game-Theor...

JPRO: Automated Multimodal Jailbreaking Via Multi-Agent Collaboration Framework

The widespread application of large VLMs makes ensuring their secure deployment critical. While recent studies have demonstrated jailbreak attacks on VLMs, existing approaches are limited: they require either white-box access, restricting practicality, or rely on manually crafted patterns, leadin...

Spectral Masking and Interpolation Attack (SMIA): a Black-Box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems

Voice Authentication Systems VAS use unique vocal characteristics for verification. They are increasingly integrated into high-security sectors such as banking and healthcare. Despite their improvements using deep learning, they face severe vulnerabilities from sophisticated threats like deepfake...

Leveraging Trustworthy AI for Automotive Security in Multi-Domain Operations: Towards a Responsive Human-AI Multi-Domain Task Force for Cyber Social Security

Multi-Domain Operations MDOs emphasize cross-domain defense against complex and synergistic threats, with civilian infrastructures like smart cities and Connected Autonomous Vehicles CAVs emerging as primary targets. As dual-use assets, CAVs are vulnerable to Multi-Surface Threats MSTs,...

Attacking Interpretable NLP Systems

Studies have shown that machine learning systems are vulnerable to adversarial examples in theory and practice. Where previous attacks have focused mainly on visual models that exploit the difference between human and machine perception, text-based models have also fallen victim to these attacks...

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS

Adversarial attacks, wherein slight inputs are carefully crafted to mislead intelligent models, have attracted increasing attention. However, a critical gap persists between theoretical advancements and practical application, particularly in structured data like network traffic, where...

IP Leakage Attacks Targeting LLM-Based Multi-Agent Systems

The rapid advancement of Large Language Models LLMs has led to the emergence of Multi-Agent Systems MAS to perform complex tasks through collaboration. However, the intricate nature of MAS, including their architecture and agent interactions, raises significant concerns regarding intellectual...

GenBreak: Red Teaming Text-To-Image Generators Using Large Language Models

Text-to-image T2I models such as Stable Diffusion have advanced rapidly and are now widely used in content creation. However, these models can be misused to generate harmful content, including nudity or violence, posing significant safety risks. While most platforms employ content moderation...

HauntAttack: When Attack Follows Reasoning As a Shadow

Emerging Large Reasoning Models LRMs consistently excel in mathematical and reasoning tasks, showcasing exceptional capabilities. However, the enhancement of reasoning abilities and the exposure of their internal reasoning processes introduce new safety vulnerabilities. One intriguing concern is:...

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples

Adversarial detection protects models from adversarial attacks by refusing suspicious test samples. However, current detection methods often suffer from weak generalization: their effectiveness tends to degrade significantly when applied to adversarially trained models rather than naturally train...

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack

Membership inference attack MIA has become one of the most widely used and effective methods for evaluating the privacy risks of machine learning models. These attacks aim to determine whether a specific sample is part of the model's training set by analyzing the model's output. While traditional...

AdInject: Real-World Black-Box Attacks on Web Agents Via Advertising Delivery

Vision-Language Model VLM based Web Agents represent a significant step towards automating complex tasks by simulating human-like interaction with websites. However, their deployment in uncontrolled web environments introduces significant security vulnerabilities. Existing research on adversarial...

Token-Level Constraint Boundary Search for Jailbreaking Text-To-Image Models

Recent advancements in Text-to-Image T2I generation have significantly enhanced the realism and creativity of generated images. However, such powerful generative capabilities pose risks related to the production of inappropriate or harmful content. Existing defense mechanisms, including prompt...