COALESCE: Economic and Security Dynamics of Skill-Based Task Outsourcing among Team of Autonomous LLM Agents

The meteoric rise and proliferation of autonomous Large Language Model LLM agents promise significant capabilities across various domains. However, their deployment is increasingly constrained by substantial computational demands, specifically for Graphics Processing Unit GPU resources. This pape...

MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models

Model extraction attacks aim to replicate the functionality of a black-box model through query access, threatening the intellectual property IP of machine-learning-as-a-service MLaaS providers. Defending against such attacks is challenging, as it must balance efficiency, robustness, and utility...

The vulnerability of Junos OS and Junos OS Evolved routers from the MX, MX304, EX9200, and PTX Series series lies in the insufficient handling of exceptional states, allowing attackers to trigger service interruptions.

The vulnerability of Junos OS and Junos OS Evolved routers from the MX, MX304, EX9200, and PTX Series series lies in the insufficient handling of exceptional states. Exploiting this vulnerability can allow a malicious actor to cause service interruptions...

CSVAR: Enhancing Visual Privacy in Federated Learning Via Adaptive Shuffling against Overfitting

Although federated learning preserves training data within local privacy domains, the aggregated model parameters may still reveal private characteristics. This vulnerability stems from clients' limited training data, which predisposes models to overfitting. Such overfitting enables models to...

Formal Security Analysis of SPV Clients Versus Home-Based Full Nodes in Bitcoin-Derived Systems

This paper presents a mathematically rigorous formal analysis of Simplified Payment Verification SPV clients, as specified in Section 8 of the original Bitcoin white paper, versus non-mining full nodes operated by home users. It defines security as resistance to divergence from global consensus a...

Sitecom WLX-2006 Wall Mount Range Extender N300 安全漏洞

The Sitecom WLX-2006 Wall Mount Range Extender N300 is a wall mount range extender from Sitecom. A security vulnerability exists in the Sitecom WLX-2006 Wall Mount Range Extender N300 v1.5 and earlier versions, which stems from a mishandling of a language cookie that could result in local file...

Dot 跨站脚本漏洞

Dot is a text-to-speech, RAG and LLM tool by alexpinel individual developers. A cross-site scripting vulnerability exists in Dot 0.9.3 and earlier versions, which stems from user input and LLM output being appended to the DOM using innerHTML, which could lead to cross-site scripting and command...

Black-Box Crypto Is Useless for Pseudorandom Codes

A pseudorandom code is a keyed error-correction scheme with the property that any polynomial number of encodings appear random to any computationally bounded adversary. We show that the pseudorandomness of any code tolerating a constant rate of random errors cannot be based on black-box reduction...

ReGA: Representation-Guided Abstraction for Model-Based Safeguarding of LLMs

Large Language Models LLMs have achieved significant success in various tasks, yet concerns about their safety and security have emerged. In particular, they pose risks in generating harmful content and vulnerability to jailbreaking attacks. To analyze and monitor machine learning models,...

Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor

Estimating the density of a distribution from its samples is a fundamental problem in statistics. Hypothesis selection addresses the setting where, in addition to a sample set, we are given $n$ candidate distributions -- referred to as hypotheses -- and the goal is to determine which one best...

Developing a Risk Identification Framework for Foundation Model Uses

As foundation models grow in both popularity and capability, researchers have uncovered a variety of ways that the models can pose a risk to the model's owner, user, or others. Despite the efforts of measuring these risks via benchmarks and cataloging them in AI risk taxonomies, there is little...

Con Instruction: Universal Jailbreaking of Multimodal Large Language Models Via Non-Textual Modalities

Existing attacks against multimodal language models MLLMs primarily communicate instructions through text accompanied by adversarial images. In contrast, we exploit the capabilities of MLLMs to interpret non-textual instructions, specifically, adversarial images or audio generated by our novel...

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges

Large Language Models LLMs still struggle with the structured reasoning and tool-assisted computation needed for problem solving in cybersecurity applications. In this work, we introduce "random-crypto", a cryptographic Capture-the-Flag CTF challenge generator framework that we use to fine-tune a...

Practical Adversarial Attacks on Stochastic Bandits Via Fake Data Injection

Adversarial attacks on stochastic bandits have traditionally relied on some unrealistic assumptions, such as per-round reward manipulation and unbounded perturbations, limiting their relevance to real-world systems. We propose a more practical threat model, Fake Data Injection, which reflects...

PYSEC-2025-54

vLLM is an inference and serving engine for large language models LLMs. In versions 0.8.0 up to but excluding 0.9.0, hitting the /v1/completions API with a invalid jsonschema as a Guided Param kills the vllm server. This vulnerability is similar GHSA-9hcf-v7m4-6m2j/CVE-2025-48943, but for regex...

PYSEC-2025-50

vLLM, an inference and serving engine for large language models LLMs, has a Regular Expression Denial of Service ReDoS vulnerability in the file vllm/entrypoints/openai/toolparsers/pythonictoolparser.py of versions 0.6.4 up to but excluding 0.9.0. The root cause is the use of a highly complex and...

CVE-2025-4986 Stored Cross-site Scripting (XSS) vulnerability affecting Model Definition in Product Manager from Release 3DEXPERIENCE R2022x through Release 3DEXPERIENCE R2025x

A stored Cross-site Scripting XSS vulnerability affecting Model Definition in Product Manager from Release 3DEXPERIENCE R2022x through Release 3DEXPERIENCE R2025x allows an attacker to execute arbitrary script code in user's browser session...

CVE-2025-4986 Stored Cross-site Scripting (XSS) vulnerability affecting Model Definition in Product Manager from Release 3DEXPERIENCE R2022x through Release 3DEXPERIENCE R2025x

A stored Cross-site Scripting XSS vulnerability affecting Model Definition in Product Manager from Release 3DEXPERIENCE R2022x through Release 3DEXPERIENCE R2025x allows an attacker to execute arbitrary script code in user's browser session...

CVE-2025-4986

CVE-2025-4986 is a stored XSS vulnerability in Dassault Systèmes’ 3DEXPERIENCE Product Manager, specifically the Model Definition feature, affecting releases from R2022x to R2025x. The captured details indicate that an attacker can execute arbitrary script in a user’s browser session via this sto...

Breaking the Gold Standard: Extracting Forgotten Data under Exact Unlearning in Large Language Models

Large language models are typically trained on datasets collected from the web, which may inadvertently contain harmful or sensitive personal information. To address growing privacy concerns, unlearning methods have been proposed to remove the influence of specific data from trained models. Of...