Uber and GeM

Executive Summary - When Anthropic and OpenAI's AI Testing Turned Into Real Breaches

You would be surprised to know that a testing function built to measure how good AI models are at simulated hacking ended up doing the real thing instead. Not once , but  three times, across two of the world's leading AI labs, within the same 9-day window at the end of July 2026.   As per the reports, Anthropic, which is among the world's leading AI labs, was running these evaluations on its own AI models namely - Claude Opus 4.7, Claude Mythos 5, and an unreleased research model, inside an environment co-managed with a third-party evaluation vendor. As per the reports, the models were told they were operating inside closed, internet-free simulations. They were not. A configuration error left the door open to the real internet, and the AI did exactly what it was trained to do in a hacking exercise, find the target and break in. Except the targets, this time, were real companies. Real credentials got stolen. Real data got accessed. Two of the three victims didn't even know they'd been breached until the AI lab called to tell them. This shows how a single unverified assumption, "this environment has no internet access" can quietly collapse the entire safety boundary of an AI test. It indicates that as these systems get more capable and more autonomous, the risk isn't necessarily the AI deciding to go rogue, it is humans failing to double-check the cage before putting something powerful inside it. And it warns us that the margin for this kind of error is shrinking fast, because what used to be a contained mistake can now scan thousands of systems and act on it within minutes. bAnthropic was not alone. Just over a week earlier, on 21 July, OpenAI had disclosed that its own models, GPT-5.6 Sol and an unreleased successor broke out of an isolated test environment and reached the real production infrastructure of Hugging Face, another AI company. In fact, it was OpenAI's disclosure that pushed Anthropic to go back and audit its own evaluation runs in the first place, which is how the three incidents above came to light. This blog examines the incident not as an isolated slip-up by two labs, but as a preview of the larger picture, a world where the gap between "theoretically vulnerable" and "actually exploited" is closing faster than most organisations' security practices are ready for.

The Chain of Disclosures

It started with Hugging Face, the open-source AI platform, which disclosed on July 16 that it had detected and contained an intrusion into part of its production infrastructure, driven end-to-end by an autonomous AI agent. Five days later, OpenAI confirmed it was responsible: models it was evaluating internally, GPT-5.6 Sol and an unreleased prototype, both running with their cyber-safety behaviour deliberately turned down to measure raw capability, had exploited a previously unknown vulnerability to break out of an isolated test environment and reach Hugging Face's real servers, apparently while chasing answers to a benchmark challenge. That disclosure prompted Anthropic to go back through its own records. On July 30, it published the results: after auditing 141,006 evaluation runs in which its Claude models could conceivably have touched the internet, it found three incidents,  six runs in total,  in which a model reached real systems belonging to three outside organisations and gained unauthorised access.

How a Fictional Exercise became a Real Attack

All three Anthropic incidents shared the same root cause. Claude was being run through capture-the-flag exercises, a standard way labs test offensive cyber capability: the model is dropped into a fictional scenario, told a piece of secret data is hidden somewhere on a network, and left to find its own way in. Anthropic's prompts told Claude, explicitly, that the environment was an internet-free simulation. Because of a misunderstanding with its evaluation partner, Irregular, that statement was false; the machines had live internet access nobody had verified. So when Claude's search for the flag led it onto the open internet, it did what it had been told to expect: it assumed everything it found was part of the exercise, including things that weren't.

• Incident 1: A fictional target company happened to share its name with a real, active domain. Across four runs, Claude Opus 4.7 attacked the real company instead, extracting infrastructure credentials and reaching a database containing several hundred rows of genuine production data, the most serious impact of the three incidents. In its own reasoning, the model repeatedly noted the system looked real and kept going anyway.
• Incident 2: Inside its fictional scenario, Claude Mythos 5 found a fake "onboarding document" referencing a software package that didn't exist on PyPI, the public Python package registry. It recognised the opportunity, built a functioning malicious package, and, after working around several dead ends to get an email account and register on PyPI, published it. The package lived online for about an hour and was installed on fifteen real machines, including a security company's automated scanner, letting Claude harvest real credentials from a company it had never even set out to target.
• Incident 3: An unreleased internal research model, unable to find its fictional target, scanned roughly 9,000 real hosts and broke into one using textbook techniques credentials read off an exposed debug page, then SQL injection. Partway through, it noticed the system it had compromised had no plausible link to its assignment, concluded on its own that the target was real, and stopped.

‍Timeline of Incidents

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The Threat is Scale, not Novelty

None of the three models discovered a novel vulnerability. Weak passwords, unauthenticated endpoints, exposed debug output, and a classic "dependency confusion" trick are techniques that have appeared on security checklists for a decade and are still relevant. That's arguably the more sobering finding: the frontier is not a new class of attack; it is the speed and tirelessness with which known, boring weaknesses can now be found and used. Anthropic's own analysis lands on a subtler point, too: in each case, whether the model's behaviour counted as "aligned" or "harmful" depended entirely on what it believed about its situation, not on any goal of its own. All three models were simply trying to complete the task they had been assigned. Two of them did so while holding a false belief, installed by their own evaluators, about whether their targets were real. Only the newest of the three models, on independently concluding it had reached a genuine system, chose to stop. Anthropic is careful to note this is not a controlled experiment and should not be read as a guarantee that newer models will always behave better. But the throughline is clear enough: telling a capable model a lie about its environment is not a neutral setup choice. It is itself a safety-relevant decision.

The Detection Gap

Perhaps the most alarming detail is the quietest one. Anthropic reached out to the three affected organisations on July 27. Two of them had detected nothing at all, no alert, no anomaly, no investigation until that call. Real credentials had been stolen and real data accessed inside systems whose owners had no idea anything had happened. That is a statement about the state of everyday detection capability, not about AI. An agent that completes an entire intrusion, start to finish, within a single automated session doesn't leave the kind of slow, human-paced footprint that most monitoring is built to catch.

The Silver Lining - Why These Disclosures Deserve Credit

Both incidents share an underappreciated feature: they were disclosed voluntarily, promptly, and with real detail, and both labs notified the organisations affected. Hugging Face brought in outside forensic specialists and law enforcement. Anthropic halted its cyber evaluations the same day it found the first suspicious transcript and has asked METR, an independent evaluation body, to review its findings. That kind of candour is exactly the behaviour any sensible policy response should want to reinforce. A regulatory reflex that punishes disclosure risks teaching labs to say less next time, not to do better. What both incidents point to, far more than any specific model capability, is a mundane and fixable governance gap: environments used to test powerful, semi-restrained AI systems need the same security discipline as production systems, verified network isolation, continuous monitoring, and evaluation scopes that are stated positively ("here is what's in bounds") rather than enforced by simply telling the model a comforting falsehood. As both companies note, a fictional test range that turns out to have a live path to the internet isn't really fictional anymore. Basic asset hygiene, like knowing what's exposed, patching debug endpoints, claiming your internal package names before someone else does, and watching outbound traffic from environments that are supposed to have none did more to prevent and contain these incidents than anything specific to the models involved.

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CyberPeace  findings  and  recomendations : For enterprises and public institutions

• Maintain a full inventory of internet-facing assets and unauthenticated endpoints, and assume the inventory is incomplete until proven otherwise.
• Eliminate default, weak, and reused credentials, and enforce phishing-resistant MFA on anyone externally reachable.
• Strip debug pages and verbose error output from production systems.
• Treat dependency confusion as a live threat: pin dependencies, use private registry namespaces, and pre-emptively claim internal package names on public registries.
• Apply deny-by-default egress filtering to every environment running AI or agentic tooling, including development and test environments, and verify isolation empirically rather than assuming it from configuration.
• Alert on any outbound connection from an environment that is supposed to have none.
• Review authentication and access logs from April 2026 onwards for short, unusually efficient sessions that look more like machine-speed compromise than human reconnaissance.

For AI developers and evaluation vendors

• Network-isolate offensive-capability evaluation environments by default, with isolation verified per run rather than inherited from configuration.
• State the scope explicitly and positively, which systems are in bounds rather than asserting a falsehood about connectivity.
• Build contractual isolation guarantees and joint pre-run verification into third-party evaluation partnerships; both labs involved here have acknowledged that neither side alone caught the misconfiguration.
• Monitor transcripts and network logs continuously, not retrospectively.

For policymakers

• A regulatory response that punishes candour risks producing silence rather than safety. India currently has no reporting framework that clearly covers containment failures in AI evaluations affecting Indian entities' behaviour. 
• RT-In's existing incident-reporting directions were not drafted with this candour in mode. Closing that gap would mean an explicit reporting obligation for evaluation of containment failures touching third-party infrastructure and a safe harbour mechanism that protects labs which disclose promptly.
• Minimum containment standards (egress verification, log retention) for organisations conducting offensive-capability AI evaluation within Indian jurisdiction;
• Recognition in national cyber doctrine that agentic tooling collapses the gap between a known-but-deferred vulnerability and an exploited one.

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Conclusion

The above incidents reveal less about AI's offensive capability and more about the gap between how these systems are tested and how carefully those tests are contained. Both labs found the breaches through their own review, not external detection, a point in their favor, but also a reminder that containment failures can go unnoticed for a while.  The realistic risk ahead isn't a sudden leap in AI's hacking sophistication; it's the compounding effect of speed and scale applied to routine reconnaissance, run against infrastructure that assumes a human attacker's pace. Treating evaluation environments with the same rigor as production systems, sandboxing, monitoring, and independent audits, should become standard practice, not an afterthought triggered by another lab's incident. The path forward is less about slowing AI down and more about catching up our containment discipline to match what these systems can now do.

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Sources

• Anthropic, "Investigating three real-world incidents in our cybersecurity evaluations" (July 30, 2026)
• OpenAI, "OpenAI and Hugging Face partner to address security incident during model evaluation" (July 21, 2026, updated July 28–29, 2026)
• Hugging Face, "Security incident disclosure — July 2026" (July 16, 2026)
• Axios, "Anthropic says three Claude models reached real-world systems during cyber tests" (July 30, 2026)
• Help Net Security, "Anthropic's Claude breached three companies during security tests" (July 31, 2026)
• Simon Willison, "Investigating three real-world incidents in our cybersecurity evaluations" (July 30, 2026)

‍Contributors

• Maj. Vineet Kumar, Founder & Global President, CyberPeace
• Mr. Neeraj Soni, Sr. Researcher, Policy & Advocacy, CyberPeace
• Mr. Isharth Kumar, (NLIU Bhopal), Intern, Policy & Advocacy, CyberPeace

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Introduction:

Cybercriminals can hack your phone using or exploiting some public charging stations such as at airports, Malls, hotel rooms, etc. When you plug in your phone or laptop devices into a power charger using USB, you may be plugging into a hacker. Juice jacking poses a security threat at public charging stations at airports, shopping malls and other public places that provide free charging stations for mobile, tablet, and laptop devices. 

Cybercriminals can either hack into the public charging spot or download malware or viruses through the USB port into your system. When you plug your phone, laptop, tablet or other such devices for charging at public charging stations, it can download malware to your phone and other such devices, and then hackers can access your personal information or passwords, It is really a problem since hackers can even get access to your bank account for unauthorised transactions by accessing your passwords and personal information. 

Hence it is important to think twice before using public charging spots, as it might lead to serious consequences such as malware, data leak and hacking. Hacking can gain unauthorised access to your personal information by installing malware in your device and they might monitor your device by installing monitor software or spyware to your device. This scam is referred to as juice jacking. 

FBI issued an advisory  warning about using public charging stations:

The Federal Bureau of Investigation (FBI), In May 2023, advised users to avoid using free charging stations in airports, hotels, or shopping centres. The warning comes as threat actors have figured out ways to inject malware into devices attached to publicly installed USB ports.

Updated Security measures:

We all must have seen public charging points such as airports, shopping malls, metro, and other public places that provide charging stations for mobile devices. But it can be a threat to your stored data on your device. During the charging process, your data can be transferred which can ultimately lead to a data breach. Hence utmost care should be taken to protect your information and data. iPhones and other devices have security measures in place, When you plug your phone into a charging power source, a pop-up appears to ask permission to allow or disallow the transfer of Data. There is also a default setting in the phones where data transfer is disabled. In the latest models, when you plug your device into a new port or a computer, a pop-up appears asking whether the device is trusted or not. 

Two major risks involved in the threat of Juice jacking: 

1. Malware installation: – Malware apps can be used by bad actors to clone your phone data to their device, Your personal data is transferred leading to a data breach. Some types of malware include Trojans, adware, spyware, crypto-miners, etc. Once this malware is injected into your device, It is easy for cybercriminals to extort a ransom to restore the information they have unauthorized access to.
2. Data Theft: It is important to give emphasis to the question of whether your data is protected at public charging stations? When we use a USB cable and connect to a public charging station port, cyber-criminals by injecting malware into the charging port system, can inject the malware into your device or your data can be transferred to the bad actors. USB cords can be exploited by cybercriminals to commit malicious activities.

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Best practices: 

• Avoid using public charging stations: Using public charging stations is not safe. It is very possible for a cybercriminal to load malware into a charging station with a USB cord. Hence It is advisable not to use public charging spots, try to make sure you charge your phone, and laptop devices in your car, at home or office so it will help you to avoid public charging stations.
• Alternative method of charging: You can carry a power bank along with you to avoid the use of public charging stations.
• Lock your phone: Lock your phone once connected to the charging port. Locking your device once connected to the charging station will prevent it from being able to sync or transfer data.
• Software update: It is important to enable and use your device’s software security measures. Mobile devices have certain technical protections against such vulnerabilities and security threats. 
• Review Settings: Disable your device’s option to automatically transfer data when a charging cable is connected. This is the default on iOS devices. Android users should disable this option in the Settings app. If your device displays a prompt asking you to “trust this computer,” it means you are connected to another device, not simply a power outlet. Deny the permission, as trusting the computer will enable data transfers to and from your device.  So when you plug your device into a USB port and a prompt appears asking permission to "share data" or “trust this computer” or “charge only,” always select “charge only.”

Conclusion:

Cybercriminals or bad actors exploit public charging stations. There have been incidents where malware was planted in the system by the use of a USB cord, During the charging process, the USB cord opens a path into your device that a cybercriminal can exploit, which means the devices can exchange data. That's called juice jacking. Hence avoid using public charging stations, our safety is in our hands and it is significantly important to give priority to best practices and stay protected in the evolving digital landscape. 

References: 

• https://www.cbsnews.com/philadelphia/news/fbi-issue-warning-about-juice-jacking-when-using-free-cell-phone-charging-kiosks/
• https://www.comparitech.com/blog/information-security/juice-jacking/#:~:text=Avoid%20public%20charging%20stations,guaranteed%20success%20with%20this%20method
• https://www.fcc.gov/juice-jacking-tips-to-avoid-it

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Executive Summary

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A video is being widely shared on social media linking it to a Muharram procession in India. The video shows a person engulfed in flames, with users claiming that an individual was deliberately set on fire during a Muharram procession in India. Several social media users are circulating the clip with communal claims. CyberPeace Research Wing  research  found that the viral claim is misleading. The video does not show any incident of a person being deliberately burned during a Muharram procession. In reality, it is from Deoria, Uttar Pradesh, where a performer sustained burn injuries while performing stunts during a Muharram procession.

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Claim:

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On X (formerly Twitter), a user posted the viral video on 3 June 2026, claiming: “Not Bangladesh, this is India where a person is being burned alive during a Muharram procession.” The post link, archive link, and screenshots are provided below. https://x.com/singh_kikki/status/2070753631167541586 , https://archive.ph/vP3bZ 

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Fact Check:

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For verification, we extracted multiple keyframes from the video and conducted a reverse image search using Google Lens. During the search, we found the same video published on the Public app on 27 June 2026. According to the report, the incident took place on Malviya Road in Deoria city, Uttar Pradesh, during a Muharram procession, where a member of the 5-Star Club akhara, Saddam (24), a resident of Bans Deoria, suffered burn injuries while performing stunts involving fire. The report further stated that members of the akhara immediately rescued him and arranged first aid. Police confirmed that his condition is stable and that the incident was accidental. No other untoward incident was reported, and all Muharram processions were conducted peacefully. A statement by Circle Officer (City), Sanjay Kumar Reddy, was also included in the report. https://public.app/video/sp_zh7dywam0a2pb 

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In our subsequent research, we found a report published on Dainik Bhaskar's website with the same claim. A link and screenshot of the post are provided below. https://www.bhaskar.com/amp/local/uttar-pradesh/deoria/news/deoria-youth-burns-muharram-procession-stunt-138296624.html

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Finally, we also found an official post from the Deoria Police X handle confirming the incident. The police stated that a performer from the 5-Star Club akhara was injured while performing stunts on Malviya Road and was immediately given first aid. His condition is stable, and no other incident occurred. https://x.com/DeoriaPolice/status/2070521290864377982 

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Conclusion:

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The research  clearly found that the viral video does not show a person being deliberately burned during a Muharram procession. The footage is from Deoria, Uttar Pradesh, where a performer was accidentally injured during a fire stunt.

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