Tags → #large language models

Security Vulnerabilities Identified in AI Code Completion Tools

A recent study has identified significant security vulnerabilities in Language Model-based Code Completion Tools, highlighting the need for improved protective measures to safeguard user data and system integrity.
Research Explores Defenses Against Prompt Injection Attacks on LLMs

Recent research highlights the vulnerabilities of large language models to prompt injection attacks and proposes various defense strategies to enhance their cybersecurity.
CKGFuzzer Enhances Fuzz Testing for Software Security

Recent research highlights advancements in software reliability and security testing, focusing on innovative methodologies for bug detection and crash prevention in mobile applications.
Enhancing Security Measures for AI Agents

Recent research has identified vulnerabilities in artificial intelligence systems and proposed defense mechanisms to enhance their security in various applications.
HyLLfuzz: New Hybrid Fuzzer Enhances Cybersecurity Testing

Researchers have introduced HyLLfuzz, a hybrid fuzzer that combines Large Language Models with traditional fuzzing techniques to enhance software testing and identify code vulnerabilities.
LLMs Enhance Vulnerability Detection in Cybersecurity Research

Recent research highlights advancements in using large language models for vulnerability detection in cybersecurity, focusing on prompt construction methodologies to enhance the identification of potential vulnerabilities in code.
AI Integration in Cybersecurity: NIDS Rules Labeling Study

Recent research has investigated the integration of artificial intelligence techniques, including Large Language Models and machine learning, to improve threat detection and response in cybersecurity.
IntelEX Framework Introduced for Enhanced Threat Detection

The IntelEX framework has been introduced as a significant advancement in cybersecurity, aimed at enhancing threat detection and response capabilities through the analysis of adversarial tactics and ethical frameworks.
Large Language Models Enhance Detection of Malicious npm Packages

Recent research explores the integration of Large Language Models (LLMs) to enhance malicious code detection in cybersecurity, highlighting both their improved detection capabilities and existing limitations.
Advancements in Scam Detection Using Large Language Models

Recent research has demonstrated advancements in scam detection using large language models, highlighting their potential while also addressing concerns related to bias and ethical considerations.