A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation

Graph Neural Networks GNNs have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings togeth...

Enhancing Adversarial Robustness of IoT Intrusion Detection Via SHAP-Based Attribution Fingerprinting

The rapid proliferation of Internet of Things IoT devices has transformed numerous industries by enabling seamless connectivity and data-driven automation. However, this expansion has also exposed IoT networks to increasingly sophisticated security threats, including adversarial attacks targeting...

Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses

With the wide application of deep reinforcement learning DRL techniques in complex fields such as autonomous driving, intelligent manufacturing, and smart healthcare, how to improve its security and robustness in dynamic and changeable environments has become a core issue in current research...

Bridging Semantics and Structure for Software Vulnerability Detection Using Hybrid Network Models

Software vulnerabilities remain a persistent risk, yet static and dynamic analyses often overlook structural dependencies that shape insecure behaviors. Viewing programs as heterogeneous graphs, we capture control- and data-flow relations as complex interaction networks. Our hybrid framework...

Explainable Ensemble Learning for Graph-Based Malware Detection

Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks GNNs have shown promise in this domain by modeling rich structural dependencies in graph-based program representations such a...

A Survey on Data Security in Large Language Models

Large Language Models LLMs, now a foundation in advancing natural language processing, power applications such as text generation, machine translation, and conversational systems. Despite their transformative potential, these models inherently rely on massive amounts of training data, often...

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...

Organizational Adaptation to Generative AI in Cybersecurity: a Systematic Review

Cybersecurity organizations are adapting to GenAI integration through modified frameworks and hybrid operational processes, with success influenced by existing security maturity, regulatory requirements, and investments in human capital and infrastructure. This qualitative research employs...

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability

Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the...

Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection

Understanding the decision-making and trusting the reliability of Deep Machine Learning Models is crucial for adopting such methods to safety-relevant applications. We extend self-explainable Prototypical Variational models with autoencoder-based out-of-distribution OOD detection: A Variational...

CVE-2025-6193

A command injection vulnerability was discovered in the TrustyAI Explainability toolkit. Arbitrary commands placed in certain fields of a LMEValJob custom resource CR may be executed in the LMEvalJob pod's terminal. This issue can be exploited via a maliciously crafted LMEvalJob by a user with...

CVE-2025-6193 Trustyai-explainability: command injection via lmevaljob cr

A command injection vulnerability was discovered in the TrustyAI Explainability toolkit. Arbitrary commands placed in certain fields of a LMEValJob custom resource CR may be executed in the LMEvalJob pod's terminal. This issue can be exploited via a maliciously crafted LMEvalJob by a user with...

CVE-2025-6193 Trustyai-explainability: command injection via lmevaljob cr

A command injection vulnerability was discovered in the TrustyAI Explainability toolkit. Arbitrary commands placed in certain fields of a LMEValJob custom resource CR may be executed in the LMEvalJob pod's terminal. This issue can be exploited via a maliciously crafted LMEvalJob by a user with...

CVE-2025-6193 Trustyai-explainability: command injection via lmevaljob cr

A command injection vulnerability was discovered in the TrustyAI Explainability toolkit. Arbitrary commands placed in certain fields of a LMEValJob custom resource CR may be executed in the LMEvalJob pod's terminal. This issue can be exploited via a maliciously crafted LMEvalJob by a user with...

CVE-2025-6193

A command injection vulnerability was discovered in the TrustyAI Explainability toolkit. Arbitrary commands placed in certain fields of a LMEValJob custom resource CR may be executed in the LMEvalJob pod's terminal. This issue can be exploited via a maliciously crafted LMEvalJob by a user with...

CVE-2025-6193

TrustyAI Explainability toolkit vulnerability CVE-2025-6193 arises from command injection in LMEvalJob CR handling. An attacker with permissions to deploy a LMEValJob CR can craft fields that escape the constructed lm_eval command, leading to arbitrary commands executed in the LMEvalJob pod termi...

CVE-2025-6193

A command injection vulnerability was discovered in the TrustyAI Explainability toolkit. Arbitrary commands placed in certain fields of a LMEValJob custom resource CR may be executed in the LMEvalJob pod's terminal. This issue can be exploited via a maliciously crafted LMEvalJob by a user with...

CVE-2025-6193: Improper Neutralization of Special Elements used in an OS Command

A command injection vulnerability was discovered in the TrustyAI Explainability toolkit. Arbitrary commands placed in certain fields of a LMEValJob custom resource CR may be executed in the LMEvalJob pod's terminal. This issue can be exploited via a maliciously crafted LMEvalJob by a user with...

PT-2025-26441

Name of the Vulnerable Software and Affected Versions: TrustyAI Explainability toolkit affected versions not specified Description: A command injection issue was discovered in the TrustyAI Explainability toolkit. This issue allows arbitrary commands placed in certain fields of a LMEValJob custom...

TrustyAI Explainability 操作系统命令注入漏洞

TrustyAI Explainability is an open source TrustyAI toolkit from TrustyAI. TrustyAI Explainability suffers from an operating system command injection vulnerability that stems from command injection and could result in an authenticated user executing arbitrary commands...