CVE-2026-21869

llama.cpp is an inference of several LLM models in C/C++. In commits 55d4206c8 and prior, the ndiscard parameter is parsed directly from JSON input in the llama.cpp server's completion endpoints without validation to ensure it's non-negative. When a negative value is supplied and the context fill...

Knowledge-To-Data: LLM-Driven Synthesis of Structured Network Traffic for Testbed-Free IDS Evaluation

Realistic, large-scale, and well-labeled cybersecurity datasets are essential for training and evaluating Intrusion Detection Systems IDS. However, they remain difficult to obtain due to privacy constraints, data sensitivity, and the cost of building controlled collection environments such as...

CVE-2025-62327

In HCL DevOps Deploy 8.1.2.0 through 8.1.2.3, a user with LLM configuration privileges may be able to recover a credential previously saved for performing authenticated LLM Queries...

CVE-2025-62327

In HCL DevOps Deploy 8.1.2.0 through 8.1.2.3, a user with LLM configuration privileges may be able to recover a credential previously saved for performing authenticated LLM Queries...

CVE-2025-62327

The CVE-2025-62327 affects HCL DevOps Deploy versions 8.1.2.0 through 8.1.2.3. A user with LLM configuration privileges may recover credentials saved for performing authenticated LLM Queries, indicating improper access control around LLM credentials. Root cause described across sources is insuffi...

LLMs, You Can Evaluate It! Design of Multi-Perspective Report Evaluation for Security Operation Centers

Security operation centers SOCs often produce analysis reports on security incidents, and large language models LLMs will likely be used for this task in the near future. We postulate that a better understanding of how veteran analysts evaluate reports, including their feedback, can help produce...

Exploit for CVE-2025-68664

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LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors

The rapid growth in both the scale and complexity of Android malware has driven the widespread adoption of machine learning ML techniques for scalable and accurate malware detection. Despite their effectiveness, these models remain vulnerable to adversarial attacks that introduce carefully crafte...

Explainable AI Agents: Capture LLM Tool Call Reasoning with Spring AI

When building AI agents with tool calling capabilities, developers often need insights into why an LLM chose a particular tool—not just which tool it selected. Understanding the model's reasoning process is important for debugging, observability, and building trustworthy AI systems. Spring AI now...

ReGAIN: Retrieval-Grounded AI Framework for Network Traffic Analysis

Modern networks generate vast, heterogeneous traffic that must be continuously analyzed for security and performance. Traditional network traffic analysis systems, whether rule-based or machine learning-driven, often suffer from high false positives and lack interpretability, limiting analyst...

dify 安全漏洞

dify is an open source LLM application development platform from LangGenius Open Source. A security vulnerability exists in version 1.5.1 of dify that stems from default credentials and could lead to unauthorized access...

Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models

This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model LLM safety evaluation from a constrained example-based approach to a more expansive and effective policy-based framework. By leveraging an attack LLM to generate a high volume of...

CVE-2025-14148

IBM UCD - IBM DevOps Deploy 8.1 through 8.1.2.3 could allow an authenticated user with LLM integration configuration privileges to recover a previously saved LLM API Token...

CVE-2025-14148 IBM DevOps Deploy is susceptible to a Insufficiently Protected Credentials vulnerability

IBM UCD - IBM DevOps Deploy 8.1 through 8.1.2.3 could allow an authenticated user with LLM integration configuration privileges to recover a previously saved LLM API Token...

From Obfuscated to Obvious: A Comprehensive JavaScript Deobfuscation Tool for Security Analysis

JavaScript's widespread adoption has made it an attractive target for malicious attackers who employ sophisticated obfuscation techniques to conceal harmful code. Current deobfuscation tools suffer from critical limitations that severely restrict their practical effectiveness. Existing tools...

Detecting Prompt Injection Attacks against Application Using Classifiers

Prompt injection attacks can compromise the security and stability of critical systems, from infrastructure to large web applications. This work curates and augments a prompt injection dataset based on the HackAPrompt Playground Submissions corpus and trains several classifiers, including LSTM,...

Taint-Based Code Slicing for LLMs-Based Malicious NPM Package Detection

The increasing sophistication of malware attacks in the npm ecosystem, characterized by obfuscation and complex logic, necessitates advanced detection methods. Recently, researchers have turned their attention from traditional detection approaches to Large Language Models LLMs due to their strong...

Automated Penetration Testing with LLM Agents and Classical Planning

While penetration testing plays a vital role in cybersecurity, achieving fully automated, hands-off-the-keyboard execution remains a significant research challenge. In this paper, we introduce the "Planner-Executor-Perceptor PEP" design paradigm and use it to systematically review existing work a...

LLM-Assisted AHP for Explainable Cyber Range Evaluation

Cyber Ranges CRs have emerged as prominent platforms for cybersecurity training and education, especially for Critical Infrastructure CI sectors that face rising cyber threats. One way to address these threats is through hands-on exercises that bridge IT and OT domains to improve defensive...

LLM-Based Vulnerable Code Augmentation: Generate or Refactor?

Vulnerability code-bases often suffer from severe imbalance, limiting the effectiveness of Deep Learning-based vulnerability classifiers. Data Augmentation could help solve this by mitigating the scarcity of under-represented CWEs. In this context, we investigate LLM-based augmentation for...