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...

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...

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...

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 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 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: 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...

PT-2025-26441 · Unknown · Trustyai Explainability Toolkit

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...

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...

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...

Striking Back at Cobalt: Using Network Traffic Metadata to Detect Cobalt Strike Masquerading Command and Control Channels

Off-the-shelf software for Command and Control is often used by attackers and legitimate pentesters looking for discretion. Among other functionalities, these tools facilitate the customization of their network traffic so it can mimic popular websites, thereby increasing their secrecy. Cobalt...

Chances and Challenges of the Model Context Protocol in Digital Forensics and Incident Response

Large language models hold considerable promise for supporting forensic investigations, but their widespread adoption is hindered by a lack of transparency, explainability, and reproducibility. This paper explores how the emerging Model Context Protocol can address these challenges and support th...

LLM-Driven APT Detection for 6G Wireless Networks: a Systematic Review and Taxonomy

Sixth Generation 6G wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and...

Interpretable Anomaly Detection in Encrypted Traffic Using SHAP with Machine Learning Models

The widespread adoption of encrypted communication protocols such as HTTPS and TLS has enhanced data privacy but also rendered traditional anomaly detection techniques less effective, as they often rely on inspecting unencrypted payloads. This study aims to develop an interpretable machine...

Unveiling the Black Box: a Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents

Reinforcement Learning RL agents are increasingly used to simulate sophisticated cyberattacks, but their decision-making processes remain opaque, hindering trust, debugging, and defensive preparedness. In high-stakes cybersecurity contexts, explainability is essential for understanding how...