CVE-2025-64321

CVE-2025-64321 concerns Salesforce Agentforce Vibes Extension with improper neutralization of input used for LLM prompting, enabling manipulation of writable configuration files. Affected versions are before 3.3.0 (also noted as before 3.2.0 in some sources). The vulnerability is tied to how prom...

CVE-2025-64321

Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Agentforce Vibes Extension allows Manipulating Writeable Configuration Files.This issue affects Agentforce Vibes Extension: before 3.3.0...

CVE-2025-64320

The vulnerability CVE-2025-64320 affects Salesforce Agentforce Vibes Extension prior to 3.2.0. The issue arises from improper neutralization of inputs used for LLM prompting, which can enable code injection via crafted prompts. Affected component: Agentforce Vibes Extension (client-side extension...

CVE-2025-64320

Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Agentforce Vibes Extension allows Code Injection.This issue affects Agentforce Vibes Extension: before 3.2.0...

CVE-2025-64318

Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Mulesoft Anypoint Code Builder allows Manipulating Writeable Configuration Files.This issue affects Mulesoft Anypoint Code Builder: before 1.12.1...

CVE-2025-10875

CVE-2025-10875 affects Salesforce Mulesoft Anypoint Code Builder before 1.11.6. The issue is improper neutralization of input used for LLM prompting, enabling possible code injection via input handling when prompting LLMs. Impact is limited to confidentiality and integrity (LOW), with network att...

CVE-2025-10875

Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Mulesoft Anypoint Code Builder allows Code Injection.This issue affects Mulesoft Anypoint Code Builder: before 1.11.6...

Is Your Prompt Poisoning Code? Defect Induction Rates and Security Mitigation Strategies

Large language models LLMs have become indispensable for automated code generation, yet the quality and security of their outputs remain a critical concern. Existing studies predominantly concentrate on adversarial attacks or inherent flaws within the models. However, a more prevalent yet...

Prompting the Priorities: A First Look at Evaluating LLMs for Vulnerability Triage and Prioritization

Security analysts face increasing pressure to triage large and complex vulnerability backlogs. Large Language Models LLMs offer a potential aid by automating parts of the interpretation process. We evaluate four models ChatGPT, Claude, Gemini, and DeepSeek across twelve prompting techniques to...

EUVD-2025-34887

A path traversal vulnerability in all versions of the Qodo Qodo Gen IDE enables a threat actor to read arbitrary local files in and outside of current projects on an end user’s system. The vulnerability can be reached directly and through indirect prompt injection...

A Systematic Study on Generating Web Vulnerability Proof-Of-Concepts Using Large Language Models

Recent advances in Large Language Models LLMs have brought remarkable progress in code understanding and reasoning, creating new opportunities and raising new concerns for software security. Among many downstream tasks, generating Proof-of-Concept PoC exploits plays a central role in vulnerabilit...

Real-VulLLM: An LLM Based Assessment Framework in the Wild

Artificial Intelligence AI and more specifically Large Language Models LLMs have demonstrated exceptional progress in multiple areas including software engineering, however, their capability for vulnerability detection in the wild scenario and its corresponding reasoning remains underexplored...

EUVD-2023-36635

Malicious code in bioql PyPI...

EUVD-2024-38673

Malicious code in bioql PyPI...

Behind the Mask: Benchmarking Camouflaged Jailbreaks in Large Language Models

Large Language Models LLMs are increasingly vulnerable to a sophisticated form of adversarial prompting known as camouflaged jailbreaking. This method embeds malicious intent within seemingly benign language to evade existing safety mechanisms. Unlike overt attacks, these subtle prompts exploit...

LLM-GUARD: Large Language Model-Based Detection and Repair of Bugs and Security Vulnerabilities in C++ and Python

Large Language Models LLMs such as ChatGPT-4, Claude 3, and LLaMA 4 are increasingly embedded in software/application development, supporting tasks from code generation to debugging. Yet, their real-world effectiveness in detecting diverse software bugs, particularly complex, security-relevant...

Phishing Detection in the Gen-AI Era: Quantized LLMs Vs Classical Models

Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency. This paper presents a comparative evaluation of traditional Machine Learning ML, Deep Learning DL, and quantized...

Improper Neutralization of Input Used for LLM Prompting

Overview @modelcontextprotocol/server-slack is a MCP server for interacting with Slack Affected versions of this package are vulnerable to Improper Neutralization of Input Used for LLM Prompting via the automatic link unfurling process. An attacker can access sensitive information by manipulating...

QGuard:Question-Based Zero-Shot Guard for Multi-Modal LLM Safety

The recent advancements in Large Language ModelsLLMs have had a significant impact on a wide range of fields, from general domains to specialized areas. However, these advancements have also significantly increased the potential for malicious users to exploit harmful and jailbreak prompts for...

Towards Effective Complementary Security Analysis Using Large Language Models

A key challenge in security analysis is the manual evaluation of potential security weaknesses generated by static application security testing SAST tools. Numerous false positives FPs in these reports reduce the effectiveness of security analysis. We propose using Large Language Models LLMs to...