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GoodVibe: Security-By-Vibe for LLM-Based Code Generation

Large language models LLMs are increasingly used for code generation in fast, informal development workflows, often referred to as vibe coding, where speed and convenience are prioritized, and security requirements are rarely made explicit. In this setting, models frequently produce functionally...

ZAST.AI Raises $6M Pre-A to Scale "Zero False Positive" AI-Powered Code Security

January 5, 2026, Seattle, USA — ZAST.AI announced the completion of a $6 million Pre-A funding round. This investment came from the well-known investment firm HH Capital, bringing ZAST.AI's total funding close to $10 million. This marks a recognition from leading capital markets of a new solution...

SecCodePRM: A Process Reward Model for Code Security

Large Language Models are rapidly becoming core components of modern software development workflows, yet ensuring code security remains challenging. Existing vulnerability detection pipelines either rely on static analyzers or use LLM/GNN-based detectors trained with coarse program-level...

CVE-2021-22888

Revive Adserver before v5.2.0 is vulnerable to a reflected XSS vulnerability in the status parameter of campaign-zone-zones.php. An attacker could trick a user with access to the user interface of a Revive Adserver instance into clicking on a specifically crafted URL and execute injected JavaScri...

Beyond Single Bugs: Benchmarking Large Language Models for Multi-Vulnerability Detection

Large Language Models LLMs have demonstrated significant potential in automated software security, particularly in vulnerability detection. However, existing benchmarks primarily focus on isolated, single-vulnerability samples or function-level classification, failing to reflect the complexity of...

AutoBaxBuilder: Bootstrapping Code Security Benchmarking

As LLMs see wide adoption in software engineering, the reliable assessment of the correctness and security of LLM-generated code is crucial. Notably, prior work has demonstrated that security is often overlooked, exposing that LLMs are prone to generating code with security vulnerabilities. These...

Architecture Patterns That Enable Cycode alternatives at Scale

Guide to scale ready code security with event driven scans unified data and API first design for large teams seeking strong growth aligned control...

WildCode: An Empirical Analysis of Code Generated by ChatGPT

LLM models are increasingly used to generate code, but the quality and security of this code are often uncertain. Several recent studies have raised alarm bells, indicating that such AI-generated code may be particularly vulnerable to cyberattacks. However, most of these studies rely on code that...

EUVD-2019-7478

Malware in sbrugna...

EUVD-2018-2448

Malware in sbrugna...

EUVD-2020-0200

Malware in sbrugna...

EUVD-2020-21424

Malware in sbrugna...

EUVD-2021-18963

Malware in sbrugna...

EUVD-2021-25517

Malware in sbrugna...

EUVD-2012-2959

Malware in sbrugna...

EUVD-2017-15059

Malware in sbrugna...

EUVD-2021-21045

Malware in sbrugna...

EUVD-2024-42278

Malicious code in bioql PyPI...

EUVD-2025-17041

Malicious code in bioql PyPI...