Denial Of Service (DoS)

github.com/rancher/rancher is vulnerable to Denial of Service DoS. The vulnerability is due to the lack of enforced request body size limits on certain public and authenticated API endpoints, which allows an attacker to send excessively large payloads that are fully loaded into memory during...

GitLab 安全漏洞

GitLab is an open source, end-to-end software development platform from GitLab, Inc. with built-in version control, issue tracking, code review, CI/CD Continuous Integration and Continuous Delivery, and other features. A security vulnerability exists in GitLab CE and EE versions prior to 18.2.7,...

PT-2025-39622

Name of the Vulnerable Software and Affected Versions GitLab CE/EE versions prior to 18.2.7 GitLab CE/EE versions 18.3 through 18.3.2 GitLab CE/EE versions 18.4 through 18.4.0 Description An issue exists that allows unauthenticated users to cause a Denial of Service DoS condition by uploading...

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense

Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies within days to bypass...

SoK: Potentials and Challenges of Large Language Models for Reverse Engineering

Reverse Engineering RE is central to software security, enabling tasks such as vulnerability discovery and malware analysis, but it remains labor-intensive and requires substantial expertise. Earlier advances in deep learning start to automate parts of RE, particularly for malware detection and...

PT-2025-39624

Name of the Vulnerable Software and Affected Versions GitLab CE/EE versions 17.4 through 18.2.6 GitLab CE/EE versions 18.3 through 18.3.2 GitLab CE/EE versions 18.4 through 18.4.0 Description Certain string conversion methods within the software demonstrate performance degradation when processing...

Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation

Fine-tuning large language models LLMs with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific domains. Given the shared characteristics in data across different organizations, the idea of collaboratively fine-tuning an LLM using data from multiple...

Investigating Security Implications of Automatically Generated Code on the Software Supply Chain

In recent years, various software supply chain SSC attacks have posed significant risks to the global community. Severe consequences may arise if developers integrate insecure code snippets that are vulnerable to SSC attacks into their products. Particularly, code generation techniques, such as...

STAF: Leveraging LLMs for Automated Attack Tree-Based Security Test Generation

In modern automotive development, security testing is critical for safeguarding systems against increasingly advanced threats. Attack trees are widely used to systematically represent potential attack vectors, but generating comprehensive test cases from these trees remains a labor-intensive,...

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning

Today's cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an urgent need for AI systems to enhance operational security work. While Large Language Models LLMs have the potential to automate and scale Security...

Bi-GRPO: Bidirectional Optimization for Jailbreak Backdoor Injection on LLMs

With the rapid advancement of large language models LLMs, their robustness against adversarial manipulations, particularly jailbreak backdoor attacks, has become critically important. Existing approaches to embedding jailbreak triggers--such as supervised fine-tuning SFT, model editing, and...

SUSE CVE-2025-9900

A flaw was found in Libtiff. This vulnerability is a "write-what-where" condition, triggered when the library processes a specially crafted TIFF image file. By providing an abnormally large image height value in the file's metadata, an attacker can trick the library into writing attacker-controll...

AZL-67794 CVE-2025-9900 affecting package libtiff for versions less than 4.6.0-9

A flaw was found in Libtiff. This vulnerability is a "write-what-where" condition, triggered when the library processes a specially crafted TIFF image file. By providing an abnormally large image height value in the file's metadata, an attacker can trick the library into writing attacker-controll...

AZL-67722 CVE-2025-9900 affecting package openjpeg2 2.3.1-12

A flaw was found in Libtiff. This vulnerability is a "write-what-where" condition, triggered when the library processes a specially crafted TIFF image file. By providing an abnormally large image height value in the file's metadata, an attacker can trick the library into writing attacker-controll...

DEBIAN-CVE-2025-9900

A flaw was found in Libtiff. This vulnerability is a "write-what-where" condition, triggered when the library processes a specially crafted TIFF image file. By providing an abnormally large image height value in the file's metadata, an attacker can trick the library into writing attacker-controll...

CVE-2025-9900

A flaw was found in Libtiff. This vulnerability is a "write-what-where" condition, triggered when the library processes a specially crafted TIFF image file. By providing an abnormally large image height value in the file's metadata, an attacker can trick the library into writing attacker-controll...

CVE-2025-9900 Libtiff: libtiff write-what-where

A flaw was found in Libtiff. This vulnerability is a "write-what-where" condition, triggered when the library processes a specially crafted TIFF image file. By providing an abnormally large image height value in the file's metadata, an attacker can trick the library into writing attacker-controll...

CVE-2025-9900 Libtiff: libtiff write-what-where

A flaw was found in Libtiff. This vulnerability is a "write-what-where" condition, triggered when the library processes a specially crafted TIFF image file. By providing an abnormally large image height value in the file's metadata, an attacker can trick the library into writing attacker-controll...

Security Bulletin: IBM Maximo Application Suite Ai-Service Component uses Starlette framework which is vulnerable to CVE-2025-54121.

Summary Security Bulletin: IBM Maximo Application Suite Ai-Service Component uses Starlette framework which is vulnerable to CVE-2025-54121. This bulletin contains information regarding the vulnerability and its fixture. Vulnerability Details CVEID:CVE-2025-54121 DESCRIPTION: Starlette is a...

Semantic-Aware Fuzzing: an Empirical Framework for LLM-Guided, Reasoning-Driven Input Mutation

Security vulnerabilities in Internet-of-Things devices, mobile platforms, and autonomous systems remain critical. Traditional mutation-based fuzzers -- while effectively explore code paths -- primarily perform byte- or bit-level edits without semantic reasoning. Coverage-guided tools such as AFL+...