Abstract--The increasing complexity of software systems and the sophistication of cyber-attacks have underscored the critical need for effective automated vulnerability detection and repair systems. Data-driven approaches using deep learning models show promise but critically depend on the availability of large, accurately labeled datasets. Y et existing datasets either suffer from noisy labels, limited range of vulnerabilities, or fail to reflect vulnerabilities as they occur in real-world software. This also limits large-scale benchmarking of such solutions. Automated vulnerability injection provides a way to directly address these dataset limitations, but existing techniques remain limited in coverage, contextual fidelity, or injection success rates. In this paper, we present A VIA TOR, the first AI-agentic vulnerability injection workflow. It automatically injects realistic, category-specific vulnerabilities for high-fidelity, diverse, large-scale vulnerability dataset generation. Unlike prior monolithic approaches, A VIA TOR orchestrates specialized AI agents, function agents and traditional code analysis tools, that replicate expert reasoning. It combines semantic analysis, injection synthesis enhanced with LoRA-based fine-tuning and Retrieval-Augmented Generation, as well as post-injection validation via static analysis and LLM-based discriminators. This modular decomposition allows specialized agents to focus on distinct tasks, improving robustness of injection and reducing error propagation across the workflow. Evaluations across three distinct benchmarks demonstrate that A VIA TOR achieves 91%-95% injection success rates, significantly surpassing existing automated dataset generation techniques in both accuracy and scope of software vulnerabilities. The rapid growth in software complexity, coupled with the sophistication of cyber-attacks, poses a significant threat to the global security and stability of digital infrastructures. In 2024 alone, the total number of publicly reported vulnerabilities rose by 25% [1]. Software vulnerabilities refer to weaknesses in system security requirements, design, implementation, or operation, that could be accidentally triggered or intentionally exploited, resulting in a violation of the system's security policy [2].

Spoken dialogue modeling introduces unique challenges beyond text-based language modeling, demanding robust turn-taking, backchanneling, and real-time interaction. Although most Spoken Dialogue Models (SDMs) rely on half-duplex processing (handling speech one turn at a time), emerging full-duplex SDMs can listen and speak simultaneously, enabling more natural and engaging conversations. However, current evaluations of such models remain limited, often focusing on turn-based metrics or high-level corpus analyses (e.g., turn gaps, pauses). To address this gap, we present Full-Duplex-Bench, a new benchmark that systematically evaluates key conversational behaviors: pause handling, backchanneling, turn-taking, and interruption management. Our framework uses automatic metrics for consistent and reproducible assessments of SDMs' interactive performance. By offering an open and standardized evaluation benchmark, we aim to advance spoken dialogue modeling and encourage the development of more interactive and natural dialogue systems.

A few weeks ago, right here on io9, readers got their first look at the cover for Christopher Paolini's newest Fractalverse novel, Fractal Noise. Almost immediately, people could see something was off about the artwork used on the cover. With some quick deduction, Twitter users realized that it was AI-generated art, which was originally posted to a stock art site by a user named Ufuk Kaya. It was also arranged by the in-house designer, who is not credited--something unusual for Tor, as the publisher often promotes the art directors and designers of each cover. After receiving widespread backlash online, Tor posted the following message on its Twitter, which explains that it licensed an image from a "reputable stock house."

Mass surveillance of the population by state agencies and corporate parties is now a well-known fact. Journalists and whistle-blowers still lack means to circumvent global spying for the sake of their investigations. With Spores, we propose a way for journalists and their sources to plan a posteriori file exchanges when they physically meet. We leverage on the multiplication of personal devices per capita to provide a lightweight, robust and fully anonymous decentralised file transfer protocol between users. Spores hinges on our novel concept of e-squads: one's personal devices, rendered intelligent by gossip communication protocols, can provide private and dependable services to their user. People's e-squads are federated into a novel onion routing network, able to withstand the inherent unreliability of personal appliances while providing reliable routing. Spores' performances are competitive, and its privacy properties of the communication outperform state of the art onion routing strategies.

HH: Semantic web, although pretty much a failed project now, is pretty interesting as it imagined we could get machines to process data in a way that is decentralized and thus extend our knowledge. The decentralized nature of Tim Berners-Lee's vision of the semantic web is very admirable. That being said, giving every single piece of data an identifier in a reliable way is unworkable with original web technology. I am quite a fan of blockchain in this respect, as it offers some level of decentralization and cryptography and thus provides a better bedrock for social computing than the traditional web. I've mostly moved on from my research of semantic web, as it's only been used by large corporations for knowledge graphs and surveillance.

Tor is one of the world's most used anonymity networks, and offers a safe haven for internet users in oppressive regimes as well as criminals operating in cyberspace. The development of Tor was partly founded by the US government to help dissidents in countries with extreme internet censorship. The FBI however allegedly harassed a Tor developer after it started being used for criminal activities. Tor is one of the most used ways for users to hide their identity online. However, the Tor network can be compromised because of vulnerabilities in the network.

Actor-Critic based approaches were among the first to address reinforcement learning in a general setting. Recently, these algorithms have gained renewed interest due to their generality, good convergence properties, and possible biological relevance. In this paper, we introduce an online temporal difference based actor-critic algorithm which is proved to converge to a neighborhood of a local maximum of the average reward. Linear function approximation is used by the critic in order estimate the value function, and the temporal difference signal, which is passed from the critic to the actor. The main distinguishing feature of the present convergence proof is that both the actor and the critic operate on a similar time scale, while in most current convergence proofs they are required to have very different time scales in order to converge. Moreover, the same temporal difference signal is used to update the parameters of both the actor and the critic. A limitation of the proposed approach, compared to results available for two time scale convergence, is that convergence is guaranteed only to a neighborhood of an optimal value, rather to an optimal value itself. The single time scale and identical temporal difference signal used by the actor and the critic, may provide a step towards constructing more biologically realistic models of reinforcement learning in the brain.