MINI-6VGX-C24M-5JJG

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MINI-FPF6-RH63-PC7C

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MINI-PXF8-9G5P-26FJ

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claudit

claudit – AI-based code security auditing tool claudit is an...

Rethinking Scanning for the AI Era: Wiz’s Agentic Code Security System

Enterprise AI AppSec requires more than powerful models. It requires a system that balances speed, depth, and cost across the software lifecycle...

Tool-Guided Retrieval-Augmented Repair for Securing LLM-Generated C Code

Large language models can generate C code from natural-language descriptions, but resulting programs often contain security vulnerabilities and compilation errors, posing risks for embedded and resource-constrained systems. This work investigates how feedback and retrieval improve reliability of...

Routing Ceilings Are Domain-Independent: Structural Prior Injection in Code Security Vulnerability Detection

Large language models LLMs exhibit a well-documented gap between latent capability and consistent activation: the router hypothesis posits that models possess the knowledge to solve a task but lack reliable internal routing to activate it. Prior work in formal mathematical reasoning SAIR, C�zares...

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs

Large Language Models LLMs are increasingly used for source code generation despite their outputs often exhibiting security vulnerabilities. Prior work shows that prompt engineering can mitigate such risks, yet 1 they focused on high-level prompting strategies, neglecting recent evidence that...

Which Neurons Detect Malicious Code? A Probing Study of LLM Security Knowledge

Background. Large language models LLMs have become increasingly capable of understanding and generating source code, leading to their widespread adoption in software engineering tasks such as code completion, repair, and vulnerability detection. However, despite their strong empirical performance...

Context-Based Adversarial Attacks on AI Code Generators: Vulnerability Analysis and Implications

AI-powered code generation systems have transformed software development but introduce critical inference-time security vulnerabilities. This research presents a systematic investigation of context-based adversarial attacks, where strategically crafted contextual inputs, including comments,...

Revisiting Vul-RAG: Reproducibility and Replicability of RAG-Based Vulnerability Detection with Open-Weight Models

Large language models LLMs have shown strong potential for automated software vulnerability detection, particularly in retrieval-augmented generation RAG settings. However, for approaches relying on proprietary models and APIs, reproducibility and replicability remain largely unexplored, raising...

How to Compare the Security of Code Written by Humans to LLM-Generated Code

Large language models LLMs are rapidly transforming how software is created and maintained. Comparing LLM-generated code against human-written standards is essential to determine whether these new tools uphold or erode the security baselines established by professional developers. Yet, we lack a...

R+R: Reassessing Java Security API Misuse in Current LLMs: A Replication on JCA and JSSE APIs with External Security Knowledge

The misuse of Java security APIs is a serious security problem in software development. Research in 2024 has shown that this problem is widespread in LLM-generated code. However, it remains unclear whether this phenomenon persists in current models and how external security knowledge affects it...

Budibase 安全漏洞

Budibase is an open-source low-code platform developed by Budibase in the UK. It allows for the creation of internal applications, workflows, and management panels within minutes. Versions of Budibase prior to 3.38.1 contained security vulnerabilities. These vulnerabilities stemmed from the V1 vi...

An Empirical Evaluation of LLM-Generated Code Security across Prompting Methods

The growing use of Large Language Models LLMs for automated code generation has enhanced software development efficiency, but often at the cost of security. Generated code frequently overlooks critical concerns, leaving it vulnerable to issues such as weak encryption and improper input validation...

Security Incentivization: An Empirical Study of How Micropayments Impact Code Security

Security often receives insufficient developer attention because it does not directly generate visible value, leading to underinvestment in practice. We evaluate a countermeasure by team-level incentives tied to measurable security improvements over time. Our semi-automated mechanism aggregates...

Bringing AI Code Security into Qualys ETM

A first-class data model for the next generation of findings AI-driven code security is becoming a real category. Anthropic's Claude Code Security and OpenAI's Codex Security are the leading examples, and more will follow. These tools reason about source code at a depth that traditional SAST cann...

Closing the Security Gap in the Age of Agentic Coding

AI agents are writing more and more code. Ensure it's secure. Learn how Wiz Code plugins and the Green Agent bring machine-speed remediation to your AI-IDE...

How exposed is your code? Find out in minutes—for free

Most security leaders share the same suspicion: there are vulnerabilities in our codebase that we don't know about. The uncomfortable truth is that most code never gets a thorough security review. Vulnerabilities accumulate quietly in active repositories, across languages and teams, often...

CVE-2026-34566 CI4MS: Pages Management Full Account Takeover for All-Roles & Privilege-Escalation via Stored DOM XSS

CI4MS is a CodeIgniter 4-based CMS skeleton that delivers a production-ready, modular architecture with RBAC authorization and theme support. Prior to version 0.31.0.0, the application fails to properly sanitize user-controlled input within the Page Management functionality when creating or editi...