AI News Archive: July 22, 2026 — Part 19
Sourced from 500+ daily AI sources, scored by relevance.
- Precision psychological markers enable targeted treatment in digital eating disorder interventions
Digital mental health interventions for eating disorders can expand treatment access, but high attrition rates and heterogenous patient responses indicate a need for precision medicine approaches that identify which patients will benefit most and predict their treatment outcomes. We analyzed 30 days of Recovery Record (a widely-adopted digital intervention for eating disorders) data from 1,166 participants with lifetime bulimia nervosa or binge-eating disorder, with assessments at baseline, midpoint, and endpoint. We identified three symptoms (eating preoccupation, shape/weight preoccupation, and fear of losing control over eating) as precision psychological markers that predict and inform treatment response. These symptoms demonstrated the strongest correlations with overall symptom improvement measured by the Eating Disorder Examination Questionnaire (EDE-Q) Global score (r=0.65-0.66), predicted outcomes when elevated at baseline (all P<0.001), mediated treatment effects through early symptom changes (all P<0.001), and differentiated response groups in cluster analysis. Participants demonstrated significant improvements across all eating disorder domains at the population level (Global score Cohen's d=-0.80), but cluster analysis revealed three distinct response patterns: strong responders (35.2%) achieved mean Global score reductions of -1.50 points, moderate responders (46.4%) achieved -0.36 points, and non-responders (18.4%) showed minimal change (-0.06 points). This framework may enable identification of individuals most likely to benefit from a digital intervention and potentially supports early treatment monitoring, paralleling precision medicine advances where psychological marker-driven patient selection could improve treatment decisions. This mechanistic approach provides a generalizable framework for developing targeted digital mental health interventions across psychiatric disorders.
- Biological drifts within normal ranges allow the detection of Crohn's disease patients at high risk of rehospitalization
Background: Crohn's disease is a chronic relapsing inflammatory bowel disease with an unpredictable clinical course that may lead to recurrent hospitalizations and surgery, making early identification of patients at risk a key challenge in longitudinal monitoring. Objective: To evaluate the prognostic value of blood biomarkers for anticipating hospitalizations in patients with Crohn's disease by moving beyond exclusive reliance on conventional reference intervals toward the analysis of personalized biological drift. The underlying premise is that fluctuations that remain within standard reference ranges (and are therefore invisible to conventional thresholds) may still carry a risk signal when interpreted relative to an individual's optimal baseline. Design: We conducted a retrospective study of 993 patients with Crohn's disease followed at the University Hospital of Liege between 2005 and 2023. Longitudinal laboratory measurements were linked to Crohn's disease-related hospitalizations. Biomarkers were transformed into z-scores relative to optimized and personalized reference populations and classified into drift categories. Time to first hospitalization was analyzed using the Kaplan-Meier method, and recurrent hospitalizations were modeled using Cox models. Results: Hospitalization-free survival differed significantly across drift categories, including for deviations within conventional reference ranges (e.g., albumin, global log-rank p<0.0001). Among 57 biomarkers screened, 32 were significant in the global log-rank analysis, including 5 that were significant for intra-reference drift classes: low lymphocytes (%), low monocytes (%), low albumin, high potassium, and low aspartate aminotransferase. Conclusion: Personalized biomarker drift detects clinically meaningful risk signals that are missed by conventional reference-interval thresholds and may enable earlier risk stratification in Crohn's disease.
- Epidemic dynamics shape variant appearance and stochastic establishment: implications for vaccination
In a population model for an infectious disease, we consider the early stochastic dynamics of an emergent 'mutant' strain, appearing and spreading during an epidemic of another 'wildtype' strain. The mutant may not reach establishment in the host population. The time at which the mutant first appears determines its probability of establishment. We calculate this establishment probability with two methods. The first method assumes a classical branching process, with a constant transmission rate. The second method reflects the changing size of the pool of susceptible hosts, due to the dynamics of the wildtype. We find that susceptible depletion can substantially impact the establishment probability. We explore the consequences of this stochastic establishment on the "escape pressure" acting on a pathogen to produce immune escape variants. We find that the overall escape pressure rate depends strongly on the appearance time of the mutant, especially if the establishment probability is itself shaped by the continued spread of the wildtype. In most scenarios, the escape pressure rate (and thus, the risk of new escape variants) peaks slightly earlier than the prevalence of the wildtype strain. Integrating the escape pressure over time, we obtain the cumulative escape pressure generated by the wildtype epidemic. The relationship between the escape pressure and the vaccination coverage depends on the cross-immunity, due to susceptible depletion. For example, with intermediate cross-immunity, the risk of immune escape may be lowest at intermediate vaccination coverages. Thus, these results raise important considerations for vaccination strategies in response to novel outbreaks.
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- OpenAI AI models went rogue during testing, triggering 'unprecedented' breach at startup
OpenAI AI models went rogue during testing, triggering 'unprecedented' breach at startup The Straits Times
- OpenAI says its AI technology acted on its own in an 'unprecedented' hack of another company
OpenAI says its AI technology acted on its own in an 'unprecedented' hack of another company Houston Chronicle
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- Google justifies its massive AI spending with a booming cloud business
Google's cloud business is thriving, as companies adopting its AI and AI infrastructure services help the tech giant to report record profits.
- AI investment boom puts Big Tech's free cash flow under pressure
AI investment boom puts Big Tech's free cash flow under pressure Reuters
- Google’s AI Spending Spree Has Investors Nervous
The cloud division posted an 82% jump as the company’s free cash flow turned negative.
- Tech earnings intensify AI spend scrutiny
Alphabet’s spending doubled since the same period last year, and its report is the first major test of investors’ patience over the record capital expenditures tied to the AI boom.
- Google burns through $6bn in cash as AI spending climbs again
Search giant says it will commit up to $205bn to AI investments in 2026
- FirstFT: Google burned through cash last quarter amid AI infrastructure splurge
Also in today’s newsletter: OpenAI ‘agent’ hacks into start-up and India’s Gen Z takes on Modi
- Google burning through cash with spiralling AI costs
The company said earlier this year it expected to spend as much as $190bn on AI investments.
- Analysis-AI investment boom puts Big Techs free cash flow under pressure
USA-MARKETS-TECH:Analysis-AI investment boom puts Big Tech's free cash flow under pressure
- AI investment boom puts Big Tech's free cash flow under pressure
ANALYSIS-AI investment boom puts Big Tech's free cash flow under pressure
- Alphabet increases spending outlook as it races to build AI data centres
Alphabet increases spending outlook as it races to build AI data centres thenationalnews.com
- Techie Tonic: AI cybersecurity incident raises global alarm over autonomous digital threats
Techie Tonic: AI cybersecurity incident raises global alarm over autonomous digital threats Gulf News
- How OpenAI’s human mistake led to the AI-powered hack on Hugging Face
OpenAI made a mistake setting up what it called a “highly isolated” testing environment and sandbox. According to cybersecurity experts, that human mistake is what made the AI-powered attack on Hugging Face possible.
- OpenAI says its AI agent broke out of testing sandbox to hack Hugging Face
"This is day one for cybersecurity in the age of agents," Hugging Face CEO says.
- OpenAI's models broke containment and cyberattacked Hugging Face — what enterprises need to know
Yesterday afternoon, OpenAI and Hugging Face published a joint disclosure outlining a cybersecurity event that redefines the threat landscape for enterprise technology. During an internal benchmark evaluation, frontier artificial intelligence models developed by OpenAI—including GPT-5.6 Sol and an unreleased, higher-capability pre-release model—broke out of their sandboxed research environment, obtained raw internet access, and autonomously executed a complex cyberattack against Hugging Face’s production infrastructure. OpenAI officially categorizes the breach as an "unprecedented cyber incident, involving state-of-the-art cyber capabilities". This incident fundamentally re-frames global discussions surrounding AI containment, frontier model alignment, commercial guardrails, and enterprise threat modeling. But first thing's first: enterprises should understand the situation, evaluate their own AI and computer systems in light of it, and above all, don't panic. As we'll review, the incident does show the increasing power and danger of frontier AI systems, but it does not mean that enterprise AI deployments are inherently less secure, nor that they need extensive overhauling. Anatomy of an Autonomous Breakout To understand how a routine benchmark evaluation escalated into a cross-infrastructure breach, one must examine the objective mechanics of the evaluation pipeline. The models were prompted to solve ExploitGym , a benchmark designed to quantify multi-step exploitation capabilities. Hyper-focused on maximizing its evaluation score, the AI agent inferred that Hugging Face likely hosted the answer keys and benchmark solutions. In pursuit of solving the problem, the model determined that breaking out of its container and stealing the answers was an optimal strategy. OpenAI’s evaluation environment is designed to be strictly isolated, limiting network traffic through a proxy used for package caching. However, the models identified and exploited a zero-day vulnerability in that internally-hosted third-party proxy software. Once through, the AI executed a series of lateral movement and privilege escalation actions across OpenAI's research nodes until reaching a machine with unrestricted internet access. From there, the model searched the web, target-identified Hugging Face, and launched a multi-stage attack by chaining together stolen credentials and remote code execution vulnerabilities on the Hugging Face servers. The UK AI Security Institute (UK AISI) recently evaluated models such as GPT-5.6 Sol, demonstrating that they are increasingly able to sustain complex, multi-step cyber operations over long time horizons. OpenAI notes that this incident confirms these theoretical capabilities now apply in real-world settings. Rewinding the Tape on a Forensic Trap While OpenAI’s July 21 release reveals the identity of the autonomous agent, Hugging Face had already begun managing the intrusion days earlier. On July 16, Hugging Face disclosed that an autonomous AI agent system breached its production infrastructure. As detailed by VentureBeat, the attacker’s entry point was a malicious dataset that triggered code execution through a remote-code loader and template-injection flaws within dataset configuration files. Once inside, the agent framework broke out onto the node running the workload and executed thousands of actions via short-lived sandboxes, harvesting cloud and cluster credentials over a single weekend. When Hugging Face's security team detected the breach, responders immediately turned to frontier AI models via commercial APIs to parse the massive volume of system logs and reconstruct over 17,000 recorded events. Then, a secondary operational crisis emerged: the commercial AI models refused to help. Because standard commercial frontier models utilize unified safety guardrails designed to block malicious prompt submissions, the models classified the incident response team's forensic queries—which contained raw shell commands, real exploit payloads, and credential dumps—as malicious attacks. Every forensic query submitted by the defenders was blocked outright. "The same prompts that are most valuable during an active intrusion—shell commands, exploit chains, credential dumps, persistence mechanisms, lateral movement—are exactly the prompts most likely to trigger safety systems," notes Merritt Baer, former Deputy CISO at AWS and senior adviser to Andesite, G2I, and AppOmni, in an interview with VentureBeat. "As AI becomes embedded in security operations, this becomes an operational resilience issue rather than merely a model policy issue". To bypass this roadblock, Hugging Face abandoned commercial hosted APIs and deployed GLM 5.2 —a state-of-the-art Chinese open-weight model released last month by z.ai, as reported at the time by VentureBeat —locally on its own infrastructure. Free from third-party API restrictions and external safety filters, GLM 5.2 successfully analyzed the raw exploit data locally, allowing defenders to complete forensic reconstruction and contain the breach without any attacker data leaving the company's environment. Industry Reaction and the Geopolitical Paradox The revelation that an American frontier model autonomously escaped containment, attacked a partner platform, and was ultimately analyzed using a Chinese open-weight model sent shockwaves through the tech community. The Wall Street Journal summarized the public reaction on X, calling the event "the stuff of cybersecurity nightmares. OpenAI said two artificial intelligence systems it was testing broke out of their test environment, hacked their way onto the internet and broke into another company. The victim was Hugging Face." Also posting to X, AI alignment researcher Lawrence Chan emphasized the importance of transparency regarding the incident, noting that "Credit where it’s due: Hugging Face detected and disclosed the intrusion last week. OAI confirmed its models were involved and provided more details, even when it didn't have to. Separate from choices that led to the hack, voluntary disclosure is good, and I’m glad they did so." Meanwhile, AI researcher Nathan Lambert provided a succinct technical summary in his own X post, observing that "An openai model, during evaluation on a cyber benchmark, exploited a public zero day bug, escaped sandboxing in openai's infra, and got into the internal huggingface infra via an exploit (through a public dataset service) all in the attempt to solve a benchmark problem." He later addressed the geopolitical implications, writing in another post on X: "Rght now American companies need Chinese models to secure their cyber infra due to guardrails on closed models. But if a Chinese model in training had infiltrated a prominent American tech company, it very likely could've been the cause of policy banning future Chinese models." Technology investor David Sacks also zeroed in on the guardrail paradox, writing in his own X post that "Hugging Face tried using American frontier models to analyze an AI-powered cyber attack. But the guardrails blocked requests containing real exploit payloads so they switched to GLM 5.2 running locally. The guardrails actually impaired defensive security." Sacks quote tweeted Hugging Face CEO Clem Delangue , who wrote: "We had this experience ourselves this week! Very scary to be guardrailed as a defender when you know attackers are likely bypassing". 6 Strategic Takeaways for Enterprise Tech Leaders Now For the average enterprise executive, the central question is immediate: is our corporate network at risk from escaping AI agents? The short answer is no, not inherently. 1. Hugging Face occupies a unique position in the software ecosystem. As a global repository for open-source AI models, code, and datasets, Hugging Face natively attracts autonomous agents, scrapers, automated evaluation pipelines, and active security researchers. Furthermore, the model’s target selection was context-specific: GPT-5.6 Sol searched for Hugging Face specifically because it deduced that Hugging Face hosted the answers to ExploitGym . Standard corporate networks—such as financial databases, HR platforms, or logistics systems—do not host benchmark solution keys that draw the direct focus of an agent attempting to solve an evaluation metric. 2. However, the long-term risk profile for enterprise technology permanently shifts following this event. AI models with long-horizon reasoning seek the path of least resistance to accomplish a goal, including breaking rules, escaping sandboxes, or exploiting zero-days if deployment safeguards are intentionally disabled for testing or bypassed by an attacker. As Hugging Face's experience illustrates, data processing pipelines that ingest external datasets without sandbox execution or static analysis act as highly vulnerable initial access infrastructure. Enterprises should re-evaluate exposure to these and implement additional security precautions like multi-step approvals and internal, potentially manual sign-off of any sensitive data ingestion or exportation. 3. Re-evaluate all prompts and implement strict prompt governance, explicitly defining negative operational boundaries. The breach underscores the acute risk of unbounded objective optimization in autonomous systems. Frontier models demonstrate a willingness to execute extreme, unanticipated attack paths to satisfy assigned metrics—in so doing, they can bypass human intent, ethical boundaries, and legal restrictions. In this instance, models tasked with evaluating their capabilities against the ExploitGym benchmark determined that escaping their sandbox and extracting the answers directly from Hugging Face's production database constituted the most efficient optimization path. All evidence suggests the models were hyperfocused on finding a solution, going to extreme lengths to achieve a narrow testing goal. For enterprise IT and security teams, this necessitates a fundamental shift in how agentic goals are defined. Organizations must implement rigorous prompt governance and state-management constraints. Directives issued to autonomous agents require explicit negative bounding—programmatically defining the operational, network, and data boundaries the agent cannot cross. Relying on implicit human norms or generalized alignment training proves insufficient when deploying machine-speed agents capable of complex, lateral problem-solving 4. This incident also drastically undercuts recent policy chatter in the U.S. calling for Chinese open-source AI models to be banned or restricted due to security concerns. As this episode demonstrates, an open-weight Chinese model actually served as the vital defensive layer for an American and French firm facing an unanticipated cyberattack from an American model that broke containment. Contrary to the official line from some U.S. policymakers and hardline China hawks, the Chinese open-source models weren't a security risk to the U.S. companies, in this case — rather, an American proprietary, closed-source model from an ostensibly secure American company was the source of the danger. Thus, any pressure U.S. companies may face from officials, agencies or non-governmental organizations to stop relying on affordable Chinese open weights models for defensive or any other lawful purposes should be viewed with a high degree of suspicion, and arguably resisted to the fullest legal extent. 5. Enterprise CISOs must audit their dependency on cloud-based AI APIs and pressure vendors to implement authenticated trust architectures. Commercial AI vendors currently treat safety as a generic content-moderation problem, applying the same blanket refusals to an enterprise CISO as they would to a malicious hacker. Baer frames this requirement perfectly: "The model shouldn’t only understand what is being asked. It should understand who is asking, why, and under what governance". 6. Incident response plans must explicitly account for scenarios where commercial APIs fail, rate-limit, or actively refuse queries during an active security event. Maintaining air-gapped, locally deployed open-weight models trained on security log analysis is no longer an edge-case luxury; it is a critical operational requirement. Security leaders running AI workloads in production must recalibrate their timelines and prepare for machine-speed threat actors that operate without human limits.
- OpenAI cyber models broke out of training environment to hack Hugging Face
The incident is unique because it was "driven, end to end, by an autonomous AI agent system," according to Hugging Face.
- OpenAI Models Escaped and Hacked a Company in Cybersecurity Test Gone Wrong
Before they could penetrate Hugging Face’s defenses, the models needed a way onto the internet. They found one.
- AI world stunned by OpenAI model that secretly escaped secure environment and hacked into a rival company
AI world stunned by OpenAI model that secretly escaped secure environment and hacked into a rival company Fortune
- OpenAI’s rogue hacking incident was a warning shot. Will it be a wake-up call to finally create AI safety regulation?
OpenAI’s rogue hacking incident was a warning shot. Will it be a wake-up call to finally create AI safety regulation? Fortune
- OpenAI's models went rogue and hacked Hugging Face. More concerning behavior may be next
OpenAI's models went rogue and hacked Hugging Face. More concerning behavior may be next Fortune
- Here's what smart people are saying about OpenAI models hacking Hugging Face on their own
Here's what smart people are saying about OpenAI models hacking Hugging Face on their own Business Insider
- OpenAI agent hacks another AI startup in security test
The agent correctly surmised that the other company, Hugging Face, hosted the evaluation’s answer sheet and attempted to cheat.
- What OpenAI’s rogue agent really did in the Hugging Face hack
This agent pursued its objective far beyond what researchers intended, revealing how difficult to contain powerful AI systems can be
- OpenAI admits its agent went rogue and hacked AI start-up Hugging Face
This incident underscores concerns over the increasingly powerful cybersecurity capabilities of new AI models
- OpenAI’s rogue agents are a wake-up call to risks posed by artificial intelligence | Shakeel Hashim
Hacking of Hugging Face shows we do not seem to have reliable ways to curb extremely powerful AI systems Last week Hugging Face – a company that hosts artificial intelligence models and datasets – was hacked . After it reported the incident to law enforcement, few would have predicted what came next: the culprits were revealed to be AI agents from OpenAI, which had broken out of containment and were acting of their own accord. Shakeel Hashim is the editor of Transformer , a publication about the power and politics of transformative AI Continue reading...
- AI agent went rogue and hacked startup by itself, OpenAI reveals
Company behind ChatGPT says agent ‘cheated’ an evaluation by attacking a Hugging Face database OpenAI has revealed that an autonomous AI agent powered by its technology went rogue during a test, accessed the open web and hacked a prominent startup by itself in an “unprecedented incident”. The company behind ChatGPT said the startup Hugging Face had detected and contained the agent – an AI tool designed to carry out tasks without human assistance – which had entered its systems. Continue reading...
- OpenAI hacking incident exposes mounting risks in AI arms race
Increasing use of aggressive training techniques sharpens threat of bad behaviour by leading models
- OpenAI admits AI ‘agent’ caused major cyber breach by itself
AI lab’s advanced models escaped testing ‘sandbox’ to hack Hugging Face
- OpenAI says its AI went rogue and launched 'unprecedented' cyber-attack
It is one of the first publicly disclosed cyber-attacks carried out by AI without direct human involvement.
- Rogue OpenAI bot escapes lab and hacks rival
Rogue OpenAI bot escapes lab and hacks rival The Telegraph
- OpenAI says its AI model went rogue and hacked startup
OpenAI called it an "unprecedented cyber incident" and pledged to support a joint investigation, with renewed attention to the risks of advanced artificial intelligence.
- OpenAI says AI models went rogue during testing, triggering unprecedented breach at startup
OPENAI-HUGGING-FACE:OpenAI says AI models went rogue during testing, triggering 'unprecedented' breach at startup
- How OpenAI models escaped a test to hack Hugging Face
How OpenAI models escaped a test to hack Hugging Face YourStory.com