198 matches found
HogVul: Black-Box Adversarial Code Generation Framework against LM-Based Vulnerability Detectors
Recent advances in software vulnerability detection have been driven by Language Model LM-based approaches. However, these models remain vulnerable to adversarial attacks that exploit lexical and syntax perturbations, allowing critical flaws to evade detection. Existing black-box attacks on...
Low Rank Comes with Low Security: Gradient Assembly Poisoning Attacks against Distributed LoRA-Based LLM Systems
Low-Rank Adaptation LoRA has become a popular solution for fine-tuning large language models LLMs in federated settings, dramatically reducing update costs by introducing trainable low-rank matrices. However, when integrated with frameworks like FedIT, LoRA introduces a critical vulnerability:...
CoTDeceptor:Adversarial Code Obfuscation against CoT-Enhanced LLM Code Agents
LLM-based code agentse.g., ChatGPT Codex are increasingly deployed as detector for code review and security auditing tasks. Although CoT-enhanced LLM vulnerability detectors are believed to provide improved robustness against obfuscated malicious code, we find that their reasoning chains and...
LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors
The rapid growth in both the scale and complexity of Android malware has driven the widespread adoption of machine learning ML techniques for scalable and accurate malware detection. Despite their effectiveness, these models remain vulnerable to adversarial attacks that introduce carefully crafte...
Real-World Adversarial Attacks on RF-Based Drone Detectors
Radio frequency RF based systems are increasingly used to detect drones by analyzing their RF signal patterns, converting them into spectrogram images which are processed by object detection models. Existing RF attacks against image based models alter digital features, making over-the-air OTA...
An Efficient Secret Communication Scheme for the Bosonic Wiretap Channel
We propose a new secret communication scheme over the bosonic wiretap channel. It uses readily available hardware such as lasers and direct photodetectors. The scheme is based on randomness extractors, pulse-position modulation, and Reed-Solomon codes and is therefore computationally efficient. I...
Adaptive Detection of Polymorphic Malware: Leveraging Mutation Engines and YARA Rules for Enhanced Security
Polymorphic malware continually alters its structure to evade signature-based defences, challenging both commercial antivirus AV and enterprise detection systems. This study introduces a reproducible framework for analysing eight polymorphic behaviours-junk code insertion, control-flow obfuscatio...
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...
Can You Trust What You See? Alpha Channel No-Box Attacks on Video Object Detection
As object detection models are increasingly deployed in cyber-physical systems such as autonomous vehicles AVs and surveillance platforms, ensuring their security against adversarial threats is essential. While prior work has explored adversarial attacks in the image domain, those attacks in the...
ABB CoreSense HM和ABB CoreSense M10 路径遍历漏洞
ABB CoreSense HM and ABB CoreSense M10 are both sensors that detect transformer oil from ABB Switzerland. A path traversal vulnerability exists in ABB CoreSense HM version 2.3.1 and earlier and ABB CoreSense M10 version 1.4.1.12 and earlier, which stems from an improperly restricted pathname and...
Intermittent File Encryption in Ransomware: Measurement, Modeling, and Detection
File encrypting ransomware increasingly employs intermittent encryption techniques, encrypting only parts of files to evade classical detection methods. These strategies, exemplified by ransomware families like BlackCat, complicate file structure based detection techniques due to diverse file...
A Hard-Label Black-Box Evasion Attack against ML-Based Malicious Traffic Detection Systems
Machine Learning ML-based malicious traffic detection is a promising security paradigm. It outperforms rule-based traditional detection by identifying various advanced attacks. However, the robustness of these ML models is largely unexplored, thereby allowing attackers to craft adversarial traffi...
RoBCtrl: Attacking GNN-Based Social Bot Detectors Via Reinforced Manipulation of Bots Control Interaction
Social networks have become a crucial source of real-time information for individuals. The influence of social bots within these platforms has garnered considerable attention from researchers, leading to the development of numerous detection technologies. However, the vulnerability and robustness...
EUVD-2015-5775
Malware in sbrugna...
EUVD-2015-5696
Malware in sbrugna...
EUVD-2008-2319
Malware in sbrugna...
EUVD-2015-7806
Malware in sbrugna...
EUVD-2015-7805
Malware in sbrugna...
NatGVD: Natural Adversarial Example Attack Towards Graph-Based Vulnerability Detection
Graph-based models learn rich code graph structural information and present superior performance on various code analysis tasks. However, the robustness of these models against adversarial example attacks in the context of vulnerability detection remains an open question. This paper proposes...