Bidirectional TLS Handshake Caching for Constrained Industrial IoT Scenarios

While TLS has become the de-facto standard for end-to-end security, its use to secure critical communication in evolving industrial IoT scenarios is severely limited by prevalent resource constraints of devices and networks. Most notably, the TLS handshake to establish secure connections incurs...

CVE-2025-7789: Use of Password Hash With Insufficient Computational Effort

A vulnerability was found in Xuxueli xxl-job up to 3.1.1 and classified as problematic. Affected by this issue is the function makeToken of the file src/main/java/com/xxl/job/admin/controller/IndexController.java of the component Token Generation. The manipulation leads to password hash with...

Generalized and Unified Equivalences between Hardness and Pseudoentropy

Pseudoentropy characterizations provide a quantitatively precise demonstration of the close relationship between computational hardness and computational randomness. We prove a unified pseudoentropy characterization that generalizes and strengthens previous results for both uniform and non-unifor...

Efficient Unlearning with Privacy Guarantees

Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning ML models trained on them. Machine unlearning has emerged as a practical means to facilitate model forgetting of data...

Adaptive Malware Detection Using Sequential Feature Selection: a Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification

Traditional malware detection methods exhibit computational inefficiency due to exhaustive feature extraction requirements, creating accuracy-efficiency trade-offs that limit real-time deployment. We formulate malware classification as a Markov Decision Process with episodic feature acquisition a...

Excessive Reasoning Attack on Reasoning LLMs

Recent reasoning large language models LLMs, such as OpenAI o1 and DeepSeek-R1, exhibit strong performance on complex tasks through test-time inference scaling. However, prior studies have shown that these models often incur significant computational costs due to excessive reasoning, such as...

CVE-2025-48931

The TeleMessage service through 2025-05-05 relies on MD5 for password hashing, which opens up various attack possibilities including rainbow tables with low computational effort...

Practical Bayes-Optimal Membership Inference Attacks

We develop practical and theoretically grounded membership inference attacks MIAs against both independent and identically distributed i.i.d. data and graph-structured data. Building on the Bayesian decision-theoretic framework of Sablayrolles et al., we derive the Bayes-optimal membership...

CVE-2025-48931

The TeleMessage service through 2025-05-05 relies on MD5 for password hashing, which opens up various attack possibilities including rainbow tables with low computational effort...

CVE-2025-48931

The TeleMessage service through 2025-05-05 relies on MD5 for password hashing, which opens up various attack possibilities including rainbow tables with low computational effort...

CVE-2023-37204

A website could have obscured the fullscreen notification by using an option element by introducing lag via an expensive computational function. This could have led to user confusion and possible spoofing attacks. This vulnerability affects Firefox 115...

CVE-2025-3937 Use of Password Hash with Insufficient Computational Effort

Use of Password Hash With Insufficient Computational Effort vulnerability in Tridium Niagara Framework on Windows, Linux, QNX, Tridium Niagara Enterprise Security on Windows, Linux, QNX allows Cryptanalysis. This issue affects Niagara Framework: before 4.14.2, before 4.15.1, before 4.10.11; Niaga...

CVE-2025-3937: Use of Password Hash With Insufficient Computational Effort

Use of Password Hash With Insufficient Computational Effort vulnerability in Tridium Niagara Framework on Windows, Linux, QNX, Tridium Niagara Enterprise Security on Windows, Linux, QNX allows Cryptanalysis. This issue affects Niagara Framework: before 4.14.2, before 4.15.1, before 4.10.11; Niaga...

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...

Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security

Federated learning FL enables collaborative model training while preserving user data privacy by keeping data local. Despite these advantages, FL remains vulnerable to privacy attacks on user updates and model parameters during training and deployment. Secure aggregation protocols have been...

Securing Immersive 360 Video Streams through Attribute-Based Selective Encryption

Delivering high-quality, secure 360� video content introduces unique challenges, primarily due to the high bitrates and interactive demands of immersive media. Traditional HTTPS-based methods, although widely used, face limitations in computational efficiency and scalability when securing these...

CB-CPIR: Code-Based Computational Private Information Retrieval

A private information retrieval PIR scheme is a protocol that allows a user to retrieve a file from a database without revealing the identity of the desired file to a curious database. Given a distributed data storage system, efficient PIR can be achieved by making assumptions about the colluding...

LLM Watermarking Using Mixtures and Statistical-To-Computational Gaps

Given a text, can we determine whether it was generated by a large language model LLM or by a human? A widely studied approach to this problem is watermarking. We propose an undetectable and elementary watermarking scheme in the closed setting. Also, in the harder open setting, where the adversar...

RevealNet: Distributed Traffic Correlation for Attack Attribution on Programmable Networks

Network attackers have increasingly resorted to proxy chains, VPNs, and anonymity networks to conceal their activities. To tackle this issue, past research has explored the applicability of traffic correlation techniques to perform attack attribution, i.e., to identify an attacker's true network...

DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-Based Intrusion Detection against Adversarial Attacks

The rapid proliferation of the Internet of Things IoT has introduced substantial security vulnerabilities, highlighting the need for robust Intrusion Detection Systems IDS. Machine learning-based intrusion detection systems ML-IDS have significantly improved threat detection capabilities; however...