535 matches found
CST-AFNet: A Dual Attention-Based Deep Learning Framework for Intrusion Detection in IoT Networks
The rapid expansion of the Internet of Things IoT has revolutionized modern industries by enabling smart automation and real time connectivity. However, this evolution has also introduced complex cybersecurity challenges due to the heterogeneous, resource constrained, and distributed nature of...
Realistic Environmental Injection Attacks on GUI Agents
GUI agents built on LVLMs are increasingly used to interact with websites. However, their exposure to open-world content makes them vulnerable to Environmental Injection Attacks EIAs that hijack agent behavior via webpage elements. Many recent studies assume the attacker to be a regular user who...
ViT-EnsembleAttack: Augmenting Ensemble Models for Stronger Adversarial Transferability in Vision Transformers
Ensemble-based attacks have been proven to be effective in enhancing adversarial transferability by aggregating the outputs of models with various architectures. However, existing research primarily focuses on refining ensemble weights or optimizing the ensemble path, overlooking the exploration ...
A Robust Cross-Domain IDS Using BiGRU-LSTM-Attention for Medical and Industrial IoT Security
The increased Internet of Medical Things IoMT and the Industrial Internet of Things IIoT interconnectivity has introduced complex cybersecurity challenges, exposing sensitive data, patient safety, and industrial operations to advanced cyber threats. To mitigate these risks, this paper introduces ...
A Transformer-Based Approach for DDoS Attack Detection in IoT Networks
DDoS attacks have become a major threat to the security of IoT devices and can cause severe damage to the network infrastructure. IoT devices suffer from the inherent problem of resource constraints and are therefore susceptible to such resource-exhausting attacks. Traditional methods for detecti...
MalFlows: Context-Aware Fusion of Heterogeneous Flow Semantics for Android Malware Detection
Static analysis, a fundamental technique in Android app examination, enables the extraction of control flows, data flows, and inter-component communications ICCs, all of which are essential for malware detection. However, existing methods struggle to leverage the semantic complementarity across...
"Energon": Unveiling Transformers from GPU Power and Thermal Side-Channels
Transformers have become the backbone of many Machine Learning ML applications, including language translation, summarization, and computer vision. As these models are increasingly deployed in shared Graphics Processing Unit GPU environments via Machine Learning as a Service MLaaS, concerns aroun...
Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection
With the rapid expansion of web-based applications and cloud services, malicious JavaScript code continues to pose significant threats to user privacy, system integrity, and enterprise security. But, detecting such threats remains challenging due to sophisticated code obfuscation techniques and...
SAEL: Leveraging Large Language Models with Adaptive Mixture-Of-Experts for Smart Contract Vulnerability Detection
With the increasing security issues in blockchain, smart contract vulnerability detection has become a research focus. Existing vulnerability detection methods have their limitations: 1 Static analysis methods struggle with complex scenarios. 2 Methods based on specialized pre-trained models...
A Novel Post-Quantum Secure Digital Signature Scheme Based on Neural Network
Digital signatures are fundamental cryptographic primitives that ensure the authenticity and integrity of digital documents. In the post-quantum era, classical public key-based signature schemes become vulnerable to brute-force and key-recovery attacks due to the computational power of quantum...
Two Views, One Truth: Spectral and Self-Supervised Features Fusion for Robust Speech Deepfake Detection
Recent advances in synthetic speech have made audio deepfakes increasingly realistic, posing significant security risks. Existing detection methods that rely on a single modality, either raw waveform embeddings or spectral based features, are vulnerable to non spoof disturbances and often overfit...
WBHT: a Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks
We propose the Wasserstein Black Hole Transformer WBHT framework for detecting black hole BH anomalies in communication networks. These anomalies cause packet loss without failure notifications, disrupting connectivity and leading to financial losses. WBHT combines generative modeling, sequential...
Explainable Vulnerability Detection in C/C++ Using Edge-Aware Graph Attention Networks
Detecting security vulnerabilities in source code remains challenging, particularly due to class imbalance in real-world datasets where vulnerable functions are under-represented. Existing learning-based methods often optimise for recall, leading to high false positive rates and reduced usability...
Frame-Level Temporal Difference Learning for Partial Deepfake Speech Detection
Detecting partial deepfake speech is essential due to its potential for subtle misinformation. However, existing methods depend on costly frame-level annotations during training, limiting real-world scalability. Also, they focus on detecting transition artifacts between bonafide and deepfake...
GHSA-2RWM-XV5J-777P
creationtimestamp| type| source ---|---|--- 2025-07-16 05:23:52+00:00| seen| https://gist.github.com/safer-bot/2b9e17cccb3cb96d421ee72a90754759 2025-07-16 08:00:50+00:00| seen| https://gist.github.com/safer-bot/fe2fd1104f8d87898cd6cbdb2c5e4073 2025-07-16 09:48:13+00:00| seen|...
GHSA-45HX-WFHJ-473X
creationtimestamp| type| source ---|---|--- 2025-07-16 03:17:48+00:00| seen| https://gist.github.com/safer-bot/f321679cb659096c5eb6a7cca02b46cc 2025-07-16 17:09:41+00:00| seen| https://gist.github.com/safer-bot/3552deb0e3e58000388a51849ac4a95b 2025-07-16 17:20:13+00:00| seen|...
CVE-2025-53031
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CVE-2025-53029
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