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...

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-2025-45146

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...

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...

ClearML Detected

This is an informational plugin to inform the user that the scanner has detected a publicly accessible ClearML instance on the target application. ClearML is an infrastructure platform for AI builders. This detection is included in the AI and LLM category. No source data...

NVIDIA Triton Detected

This is an informational plugin to inform the user that the scanner has detected a publicly accessible NVIDIA Triton instance on the target application. NVIDIA Triton provides an optimized cloud and edge inferencing solution. This detection is included in the AI and LLM category. No source data...

PT-2025-32556 · Unknown · Modelcache For Llm

Name of the Vulnerable Software and Affected Versions: ModelCache for LLM versions through 0.2.0 Description: ModelCache for LLM through version 0.2.0 contains a deserialization vulnerability in the /manager/data manager.py component. This allows attackers to execute arbitrary code by supplying...

Who'S the Evil Twin? Differential Auditing for Undesired Behavior

Detecting hidden behaviors in neural networks poses a significant challenge due to minimal prior knowledge and potential adversarial obfuscation. We explore this problem by framing detection as an adversarial game between two teams: the red team trains two similar models, one trained solely on...

VWAttacker: a Systematic Security Testing Framework for Voice over WiFi User Equipments

We present VWAttacker, the first systematic testing framework for analyzing the security of Voice over WiFi VoWiFi User Equipment UE implementations. VWAttacker includes a complete VoWiFi network testbed that communicates with Commercial-Off-The-Shelf COTS UEs based on a simple interface to test...

Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection

With the rapid expansion of web-based applications and cloud services, malicious JavaScript code continues to pose significant threats to user privacy, system integrity, and enterprise security. But, detecting such threats remains challenging due to sophisticated code obfuscation techniques and...

Enhancing Jailbreak Attacks on LLMs Via Persona Prompts

Jailbreak attacks aim to exploit large language models LLMs by inducing them to generate harmful content, thereby revealing their vulnerabilities. Understanding and addressing these attacks is crucial for advancing the field of LLM safety. Previous jailbreak approaches have mainly focused on dire...

Chaindesk Cross Site Scripting

Chaindesk, a web application for constructing AI Agents, is vulnerable to a persistent cross site scripting vulnerability in its agent chat component. An attacker can achieve arbitrary client-side script execution by crafting an AI agent whose system prompt instructs the underlying Large Language...

FaultLine: Automated Proof-Of-Vulnerability Generation Using LLM Agents

Despite the critical threat posed by software security vulnerabilities, reports are often incomplete, lacking the proof-of-vulnerability PoV tests needed to validate fixes and prevent regressions. These tests are crucial not only for ensuring patches work, but also for helping developers understa...

CERT-UA Discovers LAMEHUG Malware Linked to APT28, Using LLM for Phishing Campaign

The Computer Emergency Response Team of Ukraine CERT-UA has disclosed details of a phishing campaign that's designed to deliver a malware codenamed LAMEHUG. "An obvious feature of LAMEHUG is the use of LLM large language model, used to generate commands based on their textual representation...

LLM-Stackelberg Games: Conjectural Reasoning Equilibria and Their Applications to Spearphishing

We introduce the framework of LLM-Stackelberg games, a class of sequential decision-making models that integrate large language models LLMs into strategic interactions between a leader and a follower. Departing from classical Stackelberg assumptions of complete information and rational agents, ou...

LLMalMorph: on the Feasibility of Generating Variant Malware Using Large-Language-Models

Large Language Models LLMs have transformed software development and automated code generation. Motivated by these advancements, this paper explores the feasibility of LLMs in modifying malware source code to generate variants. We introduce LLMalMorph, a semi-automated framework that leverages...