CVE-2025-66251 Unauthenticated Path Traversal with Arbitrary File Deletion

Unauthenticated Path Traversal with Arbitrary File Deletion in DB Electronica Telecomunicazioni S.p.A. Mozart FM Transmitter versions 30, 50, 100, 300, 500, 1000, 2000, 3000, 3500, 6000, 7000 allows an attacker to perform The deletehidden parameter allows path traversal deletion of arbitrary .tgz...

CVE-2025-66250

CVE-2025-66250 affects DB Electronica Telecomunicazioni S.p.A. Mozart FM Transmitter firmware (models 30, 50, 100, 300, 500, 1000, 2000, 3000, 3500, 6000, 7000). The vulnerability is an unauthenticated arbitrary file upload via the /var/tdf/status_contents.php endpoint in the status_contents.php ...

PT-2025-48109

Name of the Vulnerable Software and Affected Versions Mozart FM Transmitter versions 30 through 7000 Description The software contains a flaw that allows for unauthenticated deletion of arbitrary files. Specifically, the deletepatch parameter within the patch contents.php file permits the deletio...

A Safety and Security Framework for Real-World Agentic Systems

This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and security are not merely fixed attributes of individual models but also emergent properties arising from the dynamic interactions among models, orchestrator...

Constructing and Benchmarking: A Labeled Email Dataset for Text-Based Phishing and Spam Detection Framework

Phishing and spam emails remain a major cybersecurity threat, with attackers increasingly leveraging Large Language Models LLMs to craft highly deceptive content. This study presents a comprehensive email dataset containing phishing, spam, and legitimate messages, explicitly distinguishing betwee...

PT-2025-48103

Name of the Vulnerable Software and Affected Versions DB Electronica Telecomunicazioni S.p.A. Mozart FM Transmitter versions 30, 50, 100, 300, 500, 1000, 2000, 3000, 3500, 6000, 7000 Description An unauthenticated path traversal issue exists that allows for arbitrary file deletion. The deletehidd...

vLLM Unverified Array Indexing DoS

The vulnerability of the vLLM library for working with large language models is related to unverified array indexing. Exploiting this vulnerability could allow a malicious actor to cause service failures...

EUVD-2025-199609

NVIDIA NeMo Framework for all platforms contains a vulnerability in the NLP and LLM components, where malicious data created by an attacker could cause code injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data...

CVE-2025-33204

CVE-2025-33204 affects NVIDIA NeMo Framework (all platforms). The vulnerability lies in the NLP/LLM components, where malicious input data can lead to code injection, with potential outcomes including code execution, privilege escalation, information disclosure, and data tampering. According to R...

CVE-2025-33204

NVIDIA NeMo Framework for all platforms contains a vulnerability in the NLP and LLM components, where malicious data created by an attacker could cause code injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data...

CVE-2025-33204

NVIDIA NeMo Framework for all platforms contains a vulnerability in the NLP and LLM components, where malicious data created by an attacker could cause code injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data...

CVE-2025-59365

A stack buffer overflow vulnerability has been identified in certain router models. An authenticated attacker may trigger this vulnerability by sending a crafted request, potentially impacting the availability of the device. Refer to the ' Security Update for ASUS Router Firmware' section on the...

CVE-2025-59365

CVE-2025-59365 corresponds to a stack buffer overflow in ASUS Router firmware affecting certain router models. According to PT-2025-48016, the issue is triggered by an authenticated attacker sending a specially crafted request, potentially causing a denial of service and impacting device availabi...

CVE-2025-59365

A stack buffer overflow vulnerability has been identified in certain router models. An authenticated attacker may trigger this vulnerability by sending a crafted request, potentially impacting the availability of the device. Refer to the ' Security Update for ASUS Router Firmware' section on the...

PT-2025-48016

Name of the Vulnerable Software and Affected Versions ASUS Router Firmware affected versions not specified Description A stack buffer overflow condition exists in certain router models. An authenticated attacker can trigger this issue by sending a specially crafted request, which may lead to a...

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...

PT-2025-48021

An authentication bypass vulnerability has been identified in the IFTTT integration feature. A remote, authenticated attacker could leverage this vulnerability to potentially gain unauthorized access to the device. This vulnerability does not affect Wi-Fi 7 series models. Refer to the 'Security...

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...

Defending Large Language Models against Jailbreak Exploits with Responsible AI Considerations

Large Language Models LLMs remain susceptible to jailbreak exploits that bypass safety filters and induce harmful or unethical behavior. This work presents a systematic taxonomy of existing jailbreak defenses across prompt-level, model-level, and training-time interventions, followed by three...

LLM-CSEC: Empirical Evaluation of Security in C/C++ Code Generated by Large Language Models

The security of code generated by large language models LLMs is a significant concern, as studies indicate that such code often contains vulnerabilities and lacks essential defensive programming constructs. This work focuses on examining and evaluating the security of LLM-generated code,...