Human-AI Collaborative Bot Detection in MMORPGs

In Massively Multiplayer Online Role-Playing Games MMORPGs, auto-leveling bots exploit automated programs to level up characters at scale, undermining gameplay balance and fairness. Detecting such bots is challenging, not only because they mimic human behavior, but also because punitive actions...

FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation

Signature-based Intrusion Detection Systems IDS detect malicious activities by matching network or host activity against predefined rules. These rules are derived from extensive Cyber Threat Intelligence CTI, which includes attack signatures and behavioral patterns obtained through automated tool...

Collaborative Intelligence: Topic Modelling of Large Language Model Use in Live Cybersecurity Operations

Objective: This work describes the topic modelling of Security Operations Centre SOC use of a large language model LLM, during live security operations. The goal is to better understand how these specialists voluntarily use this tool. Background: Human-automation teams have been extensively...

Risk Assessment and Security Analysis of Large Language Models

As large language models LLMs expose systemic security challenges in high risk applications, including privacy leaks, bias amplification, and malicious abuse, there is an urgent need for a dynamic risk assessment and collaborative defence framework that covers their entire life cycle. This paper...

CVE-2025-48956

A flaw was found in vLLM. A denial of service DoS vulnerability can be triggered by sending a single HTTP GET request with an extremely large X-Forwarded-For header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does n...

Mind the Gap: Time-Of-Check to Time-Of-Use Vulnerabilities in LLM-Enabled Agents

Large Language Model LLM-enabled agents are rapidly emerging across a wide range of applications, but their deployment introduces vulnerabilities with security implications. While prior work has examined prompt-based attacks e.g., prompt injection and data-oriented threats e.g., data exfiltration...

CVE-2025-48956

Technical details for CVE-2025-48956 are not publicly available in the provided documents. Monitor for updates from project advisories; no verified affected versions, exploit status, or remediation details are included here.

Stop LLM Attacks: How Security Helps AI Apps Achieve Their ROI

AI security is a business problem. Protect your LLM application investment and ROI by connecting your security team with business stakeholders...

PT-2026-24113

Name of the Vulnerable Software and Affected Versions vLLM versions prior to 0.15.1 vLLM version 0.17.0 Description vLLM is an inference and serving engine for large language models LLMs. A Server-Side Request Forgery SSRF protection mechanism implemented in version 0.15.1 can be bypassed in the...

CIA+TA Risk Assessment for AI Reasoning Vulnerabilities

As AI systems increasingly influence critical decisions, they face threats that exploit reasoning mechanisms rather than technical infrastructure. We present a framework for cognitive cybersecurity, a systematic protection of AI reasoning processes from adversarial manipulation. Our contributions...

Systematic Analysis of MCP Security

The Model Context Protocol MCP has emerged as a universal standard that enables AI agents to seamlessly connect with external tools, significantly enhancing their functionality. However, while MCP brings notable benefits, it also introduces significant vulnerabilities, such as Tool Poisoning...

LLM Coding Integrity Breach

Here's an interesting story about a failure being introduced by LLM-written code. Specifically, the LLM was doing some code refactoring, and when it moved a chunk of code from one file to another it changed a "break" to a "continue." That turned an error logging statement into an infinite loop,...

CVE-2025-54382 Cherry Studio RCE Vulnerability Disclosure

Cherry Studio is a desktop client that supports for multiple LLM providers. In version 1.5.1, a remote code execution RCE vulnerability exists in the Cherry Studio platform when connecting to streamableHttp MCP servers. The issue arises from the server’s implicit trust in the oauth auth redirecti...

CVE-2025-45146

ModelCache for LLM through v0.2.0 was discovered to contain an deserialization vulnerability via the component /manager/datamanager.py. This vulnerability allows attackers to execute arbitrary code via supplying crafted data...

Shadow in the Cache: Unveiling and Mitigating Privacy Risks of KV-Cache in LLM Inference

The Key-Value KV cache, which stores intermediate attention computations Key and Value pairs to avoid redundant calculations, is a fundamental mechanism for accelerating Large Language Model LLM inference. However, this efficiency optimization introduces significant yet underexplored privacy risk...

Security Bulletin: NVIDIA Megatron LM - August 2025

NVIDIA has released a software update for NVIDIA® Megatron LM. To protect your system, clone or update this software to version 0.12.2 or later from Releases · NVIDIA/Megatron-LM on GitHub. Go to NVIDIA Product Security...

CVE-2025-45146

ModelCache for LLM through v0.2.0 was discovered to contain an deserialization vulnerability via the component /manager/datamanager.py. This vulnerability allows attackers to execute arbitrary code via supplying crafted data...

CVE-2025-45146

ModelCache for LLM through v0.2.0 was discovered to contain an deserialization vulnerability via the component /manager/datamanager.py. This vulnerability allows attackers to execute arbitrary code via supplying crafted data...

BlindGuard: Safeguarding LLM-Based Multi-Agent Systems under Unknown Attacks

The security of LLM-based multi-agent systems MAS is critically threatened by propagation vulnerability, where malicious agents can distort collective decision-making through inter-agent message interactions. While existing supervised defense methods demonstrate promising performance, they may be...

CVE-2025-45146

CVE-2025-45146 applies to ModelCache for LLM through v0.2.0, where a deserialization vulnerability in /manager/data_manager.py allows arbitrary code execution via crafted data. The CVE’s CVSSv3.1 vector yields a base score of 9.8 (CRITICAL) with network attack vector, no privileges required, and ...