Anthropic said its AI model found a loophole in an old version of the Georgia state agency's website. Anthropic said its AI model found a loophole in an old version of the Georgia state agency's website. Mon 26 Oct 2026 07.03 EDTLast modified on Mon 26 Oct 2026 07.04 EDT Anthropic's Claude model accessed voter data in the US state of Georgia, the AI company has confirmed, in the latest example of unauthorised behaviour by the technology. The AI model managed to bypass a paywall to reach data that is publicly available but is typically accessible only after paying a fee. Claude accessed aggregate vote counts as part of a statistics project at Anthropic, which is preparing for a $1.5tn (£1.1tn) flotation.

The Dell Laptop Buying Guide UK: Which Dell Laptop Should You Buy? When you purchase through links in our articles, we may earn a small commission. I built an AI agent to protect my data. My vibe-coded AI assistant had strict rules about what it could and couldn't do, but a loophole left it -- and my data -- wide open to attack. I thought I was so clever.

Bad actors have been uploading fake songs onto real artist's pages on Spotify and other platforms, according to a report by 404 Media. The scheme is fairly simple. They use generative AI to whip up slop songs and then take advantage of a distribution loophole to insert them directly onto a pre-existing artist's catalog. The loophole even gives them access to proceeds generated from the tracks, as the money does not go to the original artist. The payout is also likely to be much higher than with typical AI slop, as the fake track is co-opting an artist's name and profile page.

It wasn't too long ago when the idea of an AI model or agent escaping their confines and breaking into websites on their own felt alarming. Now, it has become a pretty common story. Kimi K3, one of most powerful AI models developed by a Chinese company, also escaped its testing environment. According to US cybersecurity startup Frontier, Kimi K3 broke out of a sandbox from the UK government's AI Security Institute (AISI) while its defensive cybersecurity skills were being evaluated. Moonshot launched Kimi K3 in July and made it available for free shortly thereafter.

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The government said it would close a legal loophole in the Online Safety Act. The government said it would close a legal loophole in the Online Safety Act. Starmer to announce'crackdown on vile illegal content created by AI' after scandal involving Elon Musk's Grok tool Makers of AI chatbots that put children at risk will face massive fines or even see their services blocked in the UK under law changes to be announced by Keir Starmer on Monday. Emboldened by Elon Musk's X stopping its Grok AI tool from creating sexualised images of real people in the UK after public outrage last month, ministers are planning a "crackdown on vile illegal content created by AI". With more and more children using chatbots for everything from help with their homework to mental health support, the government said it would "move fast to shut a legal loophole and force all AI chatbot providers to abide by illegal content duties in the Online Safety Act or face the consequences of breaking the law".

Studying the responses of large language models (LLMs) to loopholes presents a two-fold opportunity. First, it affords us a lens through which to examine ambiguity and pragmatics in LLMs, since exploiting a loophole requires identifying ambiguity and performing sophisticated pragmatic reasoning. Second, loopholes pose an interesting and novel alignment problem where the model is presented with conflicting goals and can exploit ambiguities to its own advantage. To address these questions, we design scenarios where LLMs are given a goal and an ambiguous user instruction in conflict with the goal, with scenarios covering scalar implicature, structural ambiguities, and power dynamics. We then measure different models' abilities to exploit loopholes to satisfy their given goals as opposed to the goals of the user. We find that both closed-source and stronger open-source models can identify ambiguities and exploit their resulting loopholes, presenting a potential AI safety risk. Our analysis indicates that models which exploit loopholes explicitly identify and reason about both ambiguity and conflicting goals.

Language models (LMs) are susceptible to in-context reward hacking, where they exploit flaws in tainted or faulty written specifications or rubrics to achieve high scores without fulfilling the user's true intent. We introduce Specification Self-Correction (SSC), a novel, test-time framework that enables an LM to identify and correct flaws within its own guiding specification. SSC employs a multi-step inference process where the model first generates a response based on a potentially tainted specification, critiques its output, and then revises the specification itself to remove the exploitable loophole. A final, more robust response is then generated using this self-corrected specification. Across experiments spanning creative writing and agentic coding tasks with several LMs, we demonstrate that while models initially game tainted specifications in 50-70\% of cases, the SSC process reduces this vulnerability by over 90\%. This dynamic repair occurs at inference time, requires no weight modification, and leads to more robustly aligned model behavior. Code at https://github.com/vicgalle/specification-self-correction .

The legislative process is the backbone of a state built on solid institutions. Yet, due to the complexity of laws -- particularly tax law -- policies may lead to inequality and social tensions. In this study, we introduce a novel prototype system designed to address the issues of tax loopholes and tax avoidance. Our hybrid solution integrates a natural language interface with a domain-specific language tailored for planning. We demonstrate on a case study how tax loopholes and avoidance schemes can be exposed. We conclude that our prototype can help enhance social welfare by systematically identifying and addressing tax gaps stemming from loopholes.

Policymakers are increasingly using development cost and compute as proxies for AI model capabilities and risks. Recent laws have introduced regulatory requirements that are contingent on specific thresholds. However, technical ambiguities in how to perform this accounting could create loopholes that undermine regulatory effectiveness. This paper proposes seven principles for designing practical AI cost and compute accounting standards that (1) reduce opportunities for strategic gaming, (2) avoid disincentivizing responsible risk mitigation, and (3) enable consistent implementation across companies and jurisdictions.