Flock has long pretended to be a simple automated license plate reader (ALPR) company, despite overwhelming evidence that its cameras can track a lot more than that. Now there's a report that it has been developing new AI tools that can potentially locate people by how they drive, according to Wired. The software was reportedly called Nightshift and is now going by the name OS Investigate. It draws from a network of cameras in 6,000 communities and logs the movements of drivers in those communities, Wired reports. This could be used, for instance, to find a witness to a crime by analyzing vehicle movements near where a crime occurred.

OpenAI says it has slowed down training some of its most advanced AI models to improve security. In a blog post, external, the ChatGPT-maker said it was introducing new measures after its AI agents autonomously bypassed safeguards and hacked the tech start-up Hugging Face . It said training would be slowed for two weeks while it puts the upgrades in place. The capabilities of frontier models are rapidly accelerating, the company said. Our ability to understand...and secure them must stay ahead.

The ChatGPT maker says its upcoming Astra model may have reached "critical" cyber capabilities, prompting it to halt a significant number of training runs while it tightens internal safeguards. OpenAI announced Tuesday that it has halted "a significant number" of training workloads and evaluations for its forthcoming frontier artificial intelligence model--codenamed Astra--while it implements new procedures meant to address cybersecurity risks. The ChatGPT maker says it is introducing a number of new monitoring, security, and alignment requirements to better address the increasingly advanced hacking abilities of its frontier AI models . "We have to focus our energy on bringing these training runs up to those requirements and expectations. As long as it takes to get there, that's how long people are unable to proceed with their workloads," Amelia Glaese, OpenAI's vice president of research and safety, said in a briefing with reporters Tuesday.

What came into force with the EU's AI Act this week - and what didn't On August 2, the next phase of Europe's Artificial Intelligence Act came into force as the European Union frames this legislation as the world's first comprehensive law on AI. Like the General Data Protection Regulation (GDPR) before it, this new EU legislation is intended not to replace the economic bloc's existing digital rulebook but to complement it. GDPR has gone on to shape privacy practices well beyond Europe, becoming the benchmark against which many multinational organisations design their compliance programmes. The question now is whether the AI Act will prove just as influential for AI governance. What came into force this week?

"We're quite behind the ball on tackling all those risks," argues a leading AI expert pushing for more regulation. OpenAI CEO Sam Altman speaks to journalists after meeting with US House Minority Leader Hakeem Jeffries on Capitol Hill on June 3, 2026. Get your news from a source that's not owned and controlled by oligarchs. The incident sounded straight out of a science fiction movie: OpenAI's super-advanced tool hacked another AI company's systems in an attempt to pass its own Just replace the AI tech with a newly engineered virus and you have an entire existing subgenre. "We consider this incident to be an unprecedented cyber incident, involving state-of-the-art cyber capabilities," OpenAI wrote in a Tuesday blog post explaining the incident.

Modern Text-to-Image (T2I) models deploy multi-layered defenses to block NotSafe-For-Work (NSFW) content generation. These defenses typically include sequential layers such as prompt filters, concept erasers and image filters. While existing adversarial attacks have demonstrated vulnerabilities in isolated defense layers, they prove largely ineffective against multi-layered defenses deployed in real-world T2I systems. In this paper, we demonstrate that exploiting overlapping vulnerabilities across these distinct defense layers enables adversaries to systematically bypass the entire safeguard of T2I systems. We propose Transstratal Adversarial Attack (TAA), a novel black-box framework to compromise T2I models with multi-layered protection. It generates transstratal adversarial prompts to evade all defense layers simultaneously. This is accomplished through transstratal adversarial candidate generation using LLMs to fulfill implicit and subjective adversarial requirements against different defense layers, combined with adversarial genetic optimization for efficient black-box search to maximize the bypass rates and generated image harmfulness. Evaluated across 14 T2I models (e.g., Stable Diffusion, DALL E, and Midjourney) and 17 safety modules, our attack achieves an average attack success rate of 85.6%, surpassing state-of-the-art methods by 73.5%. Our findings challenge the isolated design of safety mechanisms and establish the first benchmark for holistic robustness evaluation in multi-layered safeguarded T2I models.

The latest public version of ChatGPT can be made to generate sexualised images or depict scenes of graphic violence with a simple prompt, researchers have told the BBC. British AI security startup Mindgard figured out how to make ChatGPT create graphic pictures by slightly altering a widely-shared instruction, or prompt, which was originally designed to produce humorous results. After being contacted by the BBC, ChatGPT's maker OpenAI said it had taken action to stop the chatbot responding with those types of images. After investigating this trend, we've introduced additional safeguards against this type of prompt, it said in a statement. It also said it has multiple layers of protection to prevent users making content which breaches its terms and conditions.

Large Language Models (LLMs) continue to exhibit vulnerabilities despite deliberate safety alignment efforts, posing significant risks to users and society. To safeguard against the risk of policy-violating content, system-level moderation via external guard models--designed to monitor LLM inputs and outputs and block potentially harmful content--has emerged as a prevalent mitigation strategy. Existing approaches of training guard models rely heavily on extensive human curated datasets and struggle with out-of-distribution threats, such as emerging harmful categories or jailbreak attacks. To address these limitations, we propose RSafe, an adaptive reasoning-based safeguard that conducts guided safety reasoning to provide robust protection within the scope of specified safety policies. RSafe operates in two stages: (1) guided reasoning, where it analyzes safety risks of input content through policy-guided step-by-step reasoning, and (2) reinforced alignment, where rule-based RL optimizes its reasoning paths to align with accurate safety prediction.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

By incorporating visual inputs, Multimodal Large Language Models (MLLMs) extend LLMs to support visual reasoning. However, this integration also introduces new vulnerabilities, making MLLMs susceptible to multimodal jailbreak attacks and hindering their safe deployment. Existing defense methods, including Image-to-Text Translation, Safe Prompting, and Multimodal Safety Tuning, attempt to address this by aligning multimodal inputs with LLMs' built-in safeguards. Yet, they fall short in uncovering root causes of multimodal vulnerabilities, particularly how harmful multimodal tokens trigger jailbreak in MLLMs? Consequently, they remain vulnerable to text-driven multimodal attacks, often exhibiting overdefensive behaviors and imposing heavy training overhead.