CVE-2025-54946: Improper Neutralization of Special Elements used in an SQL Command

A SQL injection vulnerability in SUNNET Corporate Training Management System before 10.11 allows remote attackers to execute arbitrary SQL commands...

CVE-2025-54945: External Control of File Name or Path

An external control of file name or path vulnerability in SUNNET Corporate Training Management System before 10.11 allows remote attackers to execute arbitrary system commands via a malicious file by controlling the destination file path...

CVE-2025-54944: Unrestricted Upload of File with Dangerous Type

An unrestricted upload of file with dangerous type vulnerability in SUNNET Corporate Training Management System before 10.11 allows remote attackers to write malicious code in a specific file, which may lead to arbitrary code execution...

CVE-2025-54943: Missing Authorization

A missing authorization vulnerability in SUNNET Corporate Training Management System before 10.11 allows remote attackers to perform unauthorized application deployment due to the absence of proper access control checks...

CVE-2025-54942: Missing Authentication for Critical Function

A missing authentication for critical function vulnerability in SUNNET Corporate Training Management System before 10.11 allows remote attackers to access deployment functionality without prior authentication...

The 5 Golden Rules of Safe AI Adoption

Employees are experimenting with AI at record speed. They are drafting emails, analyzing data, and transforming the workplace. The problem is not the pace of AI adoption, but the lack of control and safeguards in place. For CISOs and security leaders like you, the challenge is clear: you don't wa...

The 5 Golden Rules of Safe AI Adoption

Employees are experimenting with AI at record speed. They are drafting emails, analyzing data, and transforming the workplace. The problem is not the pace of AI adoption, but the lack of control and safeguards in place. For CISOs and security leaders like you, the challenge is clear: you don’t wa...

The vulnerability of the Python component of the NVIDIA Megatron-LM training library, related to improper code generation control, allows attackers to execute arbitrary code and gain elevated privileges.

Vulnerability of the Python library used for training large language models like NVIDIA Megatron-LM, due to improper code generation control. Exploiting this vulnerability allows attackers to execute arbitrary code and enhance their privileges...

Training Language Model Agents to Find Vulnerabilities with CTF-Dojo

Large language models LLMs have demonstrated exceptional capabilities when trained within executable runtime environments, notably excelling at software engineering tasks through verified feedback loops. Yet, scalable and generalizable execution-grounded environments remain scarce, limiting...

Risk Assessment and Security Analysis of Large Language Models

As large language models LLMs expose systemic security challenges in high risk applications, including privacy leaks, bias amplification, and malicious abuse, there is an urgent need for a dynamic risk assessment and collaborative defence framework that covers their entire life cycle. This paper...

Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models

Large Language Models LLMs remain vulnerable to jailbreak attacks, which attempt to elicit harmful responses from LLMs. The evolving nature and diversity of these attacks pose many challenges for defense systems, including 1 adaptation to counter emerging attack strategies without costly...

NVIDIA Megatron-LM Code Injection Vulnerability (CNVD-2025-19536)

NVIDIA Megatron-LM is a PyTorch-based distributed training framework from NVIDIA that specializes in training large Transformer language models. NVIDIA Megatron-LM suffers from a code injection vulnerability that originates in the megatron/training/arguments.py component, which can be exploited b...

When Machine Learning Meets Vulnerability Discovery: Challenges and Lessons Learned

In recent years, machine learning has demonstrated impressive results in various fields, including software vulnerability detection. Nonetheless, using machine learning to identify software vulnerabilities presents new challenges, especially regarding the scale of data involved, which was not a...

CVE-2025-50461

A deserialization vulnerability exists in Volcengine's verl 3.0.0, specifically in the scripts/modelmerger.py script when using the "fsdp" backend. The script calls torch.load with weightsonly=False on user-supplied .pt files, allowing attackers to execute arbitrary code if a maliciously crafted...

microcode_ctl: From CVEorg collector

New Spectre-v2 attack classes have been discovered within CPU architectures that enable self-training exploitation of speculative execution within the same privilege domain. These novel techniques bypass existing hardware and software mitigations, including IBPB, eIBRS, and BHINO, by leveraging...

Unlearning at Scale: Implementing the Right to Be Forgotten in Large Language Models

We study the right to be forgotten GDPR Art. 17 for large language models and frame unlearning as a reproducible systems problem. Our approach treats training as a deterministic program and logs a minimal per-microbatch record ordered ID hash, RNG seed, learning-rate value, optimizer-step counter...

RMSL: Weakly-Supervised Insider Threat Detection with Robust Multi-Sphere Learning

Insider threat detection aims to identify malicious user behavior by analyzing logs that record user interactions. Due to the lack of fine-grained behavior-level annotations, detecting specific behavior-level anomalies within user behavior sequences is challenging. Unsupervised methods face high...

CVE-2025-23306

NVIDIA Megatron-LM for all platforms contains a vulnerability in the megatron/training/ arguments.py component where an attacker could cause a code injection issue by providing a malicious input. A successful exploit of this vulnerability may lead to code execution, escalation of privileges,...

CVE-2025-23306

NVIDIA Megatron-LM for all platforms contains a vulnerability in the megatron/training/ arguments.py component where an attacker could cause a code injection issue by providing a malicious input. A successful exploit of this vulnerability may lead to code execution, escalation of privileges,...

CVE-2025-23306

CVE-2025-23306 affects NVIDIA Megatron-LM, specifically the megatron/training/arguments.py component. The vulnerability permits code injection through malicious input, with potential outcomes including code execution, privilege escalation, information disclosure, and data tampering. Public refere...