PT-2025-32493 · Pypi · Ms-Swift

This appears to be a security vulnerability report describing a remote code execution RCE exploit in the ms-swift framework through malicious pickle deserialization in adapter model files. The vulnerability allows arbitrary command execution when loading specially crafted adapter models from...

SAEL: Leveraging Large Language Models with Adaptive Mixture-Of-Experts for Smart Contract Vulnerability Detection

With the increasing security issues in blockchain, smart contract vulnerability detection has become a research focus. Existing vulnerability detection methods have their limitations: 1 Static analysis methods struggle with complex scenarios. 2 Methods based on specialized pre-trained models...

How Microsoft defends against indirect prompt injection attacks

Summary The growing adoption of large language models LLMs in enterprise workflows has introduced a new class of adversarial techniques: indirect prompt injection. Indirect prompt injection can be used against systems that leverage large language models LLMs to process untrusted data...

ZIUM: Zero-Shot Intent-Aware Adversarial Attack on Unlearned Models

Machine unlearning MU removes specific data points or concepts from deep learning models to enhance privacy and prevent sensitive content generation. Adversarial prompts can exploit unlearned models to generate content containing removed concepts, posing a significant security risk. However,...

Invisible Injections: Exploiting Vision-Language Models through Steganographic Prompt Embedding

Vision-language models VLMs have revolutionized multimodal AI applications but introduce novel security vulnerabilities that remain largely unexplored. We present the first comprehensive study of steganographic prompt injection attacks against VLMs, where malicious instructions are invisibly...

Secure Coding for Web Applications: Frameworks, Challenges, and the Role of LLMs

Secure coding is a critical yet often overlooked practice in software development. Despite extensive awareness efforts, real-world adoption remains inconsistent due to organizational, educational, and technical barriers. This paper provides a comprehensive review of secure coding practices across...

Can We End the Cat-And-Mouse Game? Simulating Self-Evolving Phishing Attacks with LLMs and Genetic Algorithms

Anticipating emerging attack methodologies is crucial for proactive cybersecurity. Recent advances in Large Language Models LLMs have enabled the automated generation of phishing messages and accelerated research into potential attack techniques. However, predicting future threats remains...

Securing Cloud AI and LLMs with TotalAI for Visibility, Risk Context and Control

As enterprises accelerate AI adoption, large language models LLMs hosted on public cloud platforms are quickly becoming the norm due to their simplified access and pricing model. Cloud-native services like AWS Bedrock, Azure AI Foundry, and Google Vertex AI offer powerful, pay-as-you-go access to...

CVE-2025-54413

A flaw was found in skops. An inconsistency in MethodNode allows access to unexpected object fields through dot notation when a specially crafted model file is loaded. This issue allows arbitrary code execution at load time...

The vulnerability of Ollama’s system for running and managing large language models lies in its lack of proper input data validation, allowing attackers to execute arbitrary code.

The vulnerability of Ollama’s system for running and managing large language models is related to insufficient validation of input data. Exploiting this vulnerability could allow a remote attacker to execute arbitrary code...

The vulnerability of the framework for working with large language models (LLMs) like LlamaIndex lies in the improper restriction on recursive references to entities in the DTD. This allows attackers to trigger a service failure.

The vulnerability of the LlamaIndex framework for working with large language models is related to an improper limitation on recursive references to entities in the DTD. Exploiting this vulnerability could allow a malicious actor to cause service failures...

Cascading and Proxy Membership Inference Attacks

A Membership Inference Attack MIA assesses how much a trained machine learning model reveals about its training data by determining whether specific query instances were included in the dataset. We classify existing MIAs into adaptive or non-adaptive, depending on whether the adversary is allowed...

EdgeAgentX-DT: Integrating Digital Twins and Generative AI for Resilient Edge Intelligence in Tactical Networks

We introduce EdgeAgentX-DT, an advanced extension of the EdgeAgentX framework that integrates digital twin simulations and generative AI-driven scenario training to significantly enhance edge intelligence in military networks. EdgeAgentX-DT utilizes network digital twins, virtual replicas...

SDD: Self-Degraded Defense against Malicious Fine-Tuning

Open-source Large Language Models LLMs often employ safety alignment methods to resist harmful instructions. However, recent research shows that maliciously fine-tuning these LLMs on harmful data can easily bypass these safeguards. To counter this, we theoretically uncover why malicious fine-tuni...

Information Exposure

Overview Affected versions of this package are vulnerable to Information Exposure via the q URL parameter in the /api/v2.0/users endpoint. An attacker can retrieve sensitive password hash and salt values by abusing the filtering capability to extract this information character by character. Note:...

Subliminal Learning in AIs

Today's freaky LLM behavior: We study subliminal learning, a surprising phenomenon where language models learn traits from model-generated data that is semantically unrelated to those traits. For example, a "student" model learns to prefer owls when trained on sequences of numbers generated by a...

Enhancing IoT Intrusion Detection Systems through Adversarial Training

The augmentation of Internet of Things IoT devices transformed both automation and connectivity but revealed major security vulnerabilities in networks. We address these challenges by designing a robust intrusion detection system IDS to detect complex attacks by learning patterns from the...

PrompTrend: Continuous Community-Driven Vulnerability Discovery and Assessment for Large Language Models

Static benchmarks fail to capture LLM vulnerabilities emerging through community experimentation in online forums. We present PrompTrend, a system that collects vulnerability data across platforms and evaluates them using multidimensional scoring, with an architecture designed for scalable...

OneShield -- the Next Generation of LLM Guardrails

The rise of Large Language Models has created a general excitement about the great potential for a myriad of applications. While LLMs offer many possibilities, questions about safety, privacy, and ethics have emerged, and all the key actors are working to address these issues with protective...

CVE-2025-4395

Medtronic MyCareLink Patient Monitor has a built-in user account with an empty password, which allows an attacker with physical access to log in with no password and access modify system functionality. This issue affects MyCareLink Patient Monitor models 24950 and 24952: before June 25, 2025...