ProxyPrompt: Securing System Prompts against Prompt Extraction Attacks

The integration of large language models LLMs into a wide range of applications has highlighted the critical role of well-crafted system prompts, which require extensive testing and domain expertise. These prompts enhance task performance but may also encode sensitive information and filtering...

AutoRAN: Weak-To-Strong Jailbreaking of Large Reasoning Models

This paper presents AutoRAN, the first automated, weak-to-strong jailbreak attack framework targeting large reasoning models LRMs. At its core, AutoRAN leverages a weak, less-aligned reasoning model to simulate the target model's high-level reasoning structures, generates narrative prompts, and...

WASP: Benchmarking Web Agent Security against Prompt Injection Attacks

Autonomous UI agents powered by AI have tremendous potential to boost human productivity by automating routine tasks such as filing taxes and paying bills. However, a major challenge in unlocking their full potential is security, which is exacerbated by the agent's ability to take action on their...

Schneider Electric EcoStruxure Power Build Rapsody

RISK EVALUATION Successful exploitation of this vulnerability could allow an attacker to execute arbitrary code. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability, such as: Minimize network exposure for all control...

ECOVACS DEEBOT Vacuum and Base Station (Update A)

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to send malicious updates to the devices or execute code. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities, such as:...

A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network

Connected and Autonomous Vehicles CAVs enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network CAN bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robus...

Enhancing IoT Cyber Attack Detection in the Presence of Highly Imbalanced Data

Due to the rapid growth in the number of Internet of Things IoT networks, the cyber risk has increased exponentially, and therefore, we have to develop effective IDS that can work well with highly imbalanced datasets. A high rate of missed threats can be the result, as traditional machine learnin...

SecReEvalBench: a Multi-Turned Security Resilience Evaluation Benchmark for Large Language Models

The increasing deployment of large language models in security-sensitive domains necessitates rigorous evaluation of their resilience against adversarial prompt-based attacks. While previous benchmarks have focused on security evaluations with limited and predefined attack domains, such as...

Private Transformer Inference in MLaaS: a Survey

Transformer models have revolutionized AI, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service MLaaS raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private...

Multiparty Selective Disclosure Using Attribute-Based Encryption

This study proposes a mechanism for encrypting SD-JWT Selective Disclosure JSON Web Token Disclosures using Attribute-Based Encryption ABE to enable flexible access control on the basis of the Verifier's attributes. By integrating Ciphertext-Policy ABE CP-ABE into the existing SD-JWT framework, t...

Red Teaming the Mind of the Machine: a Systematic Evaluation of Prompt Injection and Jailbreak Vulnerabilities in LLMs

Large Language Models LLMs are increasingly integrated into consumer and enterprise applications. Despite their capabilities, they remain susceptible to adversarial attacks such as prompt injection and jailbreaks that override alignment safeguards. This paper provides a systematic investigation o...

MUBox: a Critical Evaluation Framework of Deep Machine Unlearning

Recent legal frameworks have mandated the right to be forgotten, obligating the removal of specific data upon user requests. Machine Unlearning has emerged as a promising solution by selectively removing learned information from machine learning models. This paper presents MUBox, a comprehensive...

Assessing the Latency of Network Layer Security in 5G Networks

In contrast to its predecessors, 5G supports a wide range of commercial, industrial, and critical infrastructure scenarios. One key feature of 5G, ultra-reliable low latency communication, is particularly appealing to such scenarios for its real-time capabilities. However, 5G's enhanced security,...

Mirror Mirror on the Wall, Have I Forgotten It All? A New Framework for Evaluating Machine Unlearning

Machine unlearning methods take a model trained on a dataset and a forget set, then attempt to produce a model as if it had only been trained on the examples not in the forget set. We empirically show that an adversary is able to distinguish between a mirror model a control model produced by...

ABAC Lab: an Interactive Platform for Attribute-Based Access Control Policy Analysis, Tools, and Datasets

Attribute-Based Access Control ABAC provides expressiveness and flexibility, making it a compelling model for enforcing fine-grained access control policies. To facilitate the transition to ABAC, extensive research has been conducted to develop methodologies, frameworks, and tools that assist...

Evaluating Explanation Quality in X-IDS Using Feature Alignment Metrics

Explainable artificial intelligence XAI methods have become increasingly important in the context of explainable intrusion detection systems X-IDSs for improving the interpretability and trustworthiness of X-IDSs. However, existing evaluation approaches for XAI focus on model-specific properties...

CVE-2025-37870

In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: prevent hang on link training fail Why When link training fails, the phy clock will be disabled. However, in enablestreams, it is assumed that link training succeeded and the mux selects the phy clock, causing a...

PT-2025-21905 · Git +1 · Quickjs

Name of the Vulnerable Software and Affected Versions: The product name cannot be determined. affected versions not specified Description: The software suffers from a heap-buffer-overflow read issue. The crash occurs during JS CallInternal, JS EvalFunctionInternal, and JS EvalInternal function...

Security Steerability Is All You Need

The adoption of Generative AI GenAI in various applications inevitably comes with expanding the attack surface, combining new security threats along with the traditional ones. Consequently, numerous research and industrial initiatives aim to mitigate these security threats in GenAI by developing...

Rego Code Injection

github.com/open-policy-agent/opa is vulnerable to Rego code injection. The vulnerability is due to unsanitized HTTP request paths being used to construct Rego queries during policy evaluation, allowing attackers to inject Rego code...