It won’t work:

My suspicion was that GPT 5.6-Cyber would succeed, but the frequency and manner of its success removed all doubt. We have to reassess sandboxing quality for capable AI agents, and in general the software stack with which they interact.

An off-the-shelf VM is not enough to contain a modern, cyber-capable AI agent. There is simply too much attack surface. Even innocuous features (like running with a display) add extra, exploitable attack surface.

This is interesting:

The records describe a force-generation mechanism for several General Staff components, including the GRU, Main Operational Directorate, and 8th Directorate, which is associated with protected communications, cryptography, and information security.

[…]

The reporting also linked a 2024 Department No. 4 graduate, Aleksei Kondrashov, to Military Unit 74455, widely known as Sandworm.

That unit has been associated with destructive cyber activity against Ukraine and other targets, including the 2017 NotPetya attack.

The reports do not establish that every listed graduate participated in a named operation; assignments should therefore be described as reported unit placements, not proof of individual operational involvement.

The Bauman material reframes Russia’s cyber capability as an institutional system, not merely a collection of well-known threat groups.

It suggests that Moscow has formalized a recurring pathway from university recruitment to military service, where students receive supervised technical and ideological preparation before entering intelligence, cyber, and security roles.

For defenders, the leak reinforces the need to track Russian operations as a combined threat: espionage, destructive activity, military reconnaissance, technical surveillance, and influence campaigns may draw on related personnel pipelines and overlapping doctrine.

The exposure of Department No. 4 also provides researchers with a clearer lens for understanding how the GRU sustains cyber capacity beyond the familiar APT28 and Sandworm brand names.

The AI Security Institute has a new report of AI systems engaging in “unsanctioned behavior”—what I have been calling “genie behavior—while being tested on their cybersecurity capabilities.

The incident stemmed from a single evaluation where agents were given a task of solving a cyber security challenge. We ran this challenge 122 times across several models. Our investigation found that in 10 of those runs, an AI agent took autonomous, unsanctioned action on the live internet, targeting real people and organisations. In total, we catalogued 19 such actions. Almost all of this behaviour (17 actions) came from a single model, Anthropic’s Mythos 5, with 2 actions involving OpenAI’s GPT-5.6-Sol with cyber classifiers (mechanisms to prevent misuse) disabled. In the most serious case, an agent tried to insert malicious code into an open-source project. In an attempt to get the code approved, the agent engaged in social engineering—creating fake online identities and using them to pressure the project’s maintainer to approve the code. A human maintainer caught and refused to approve the malicious code.

[…]

Below, we highlight the four most significant behaviours observed. A full summary of cases is available in our technical incident report.

1. An attempted supply-chain attack on real open-source software. In the most serious sequence, an agent tried to insert malicious code into a publicly used open-source project and took actions in an attempt to secure approval for this insertion by human reviewers. The agent researched the project’s human maintainers, created multiple fake identities, and used the fake identities to socially engineer a real maintainer into approving the code. When the agent’s pull request was challenged in public, it edited its earlier activity to appear harmless and considered adopting a fresh identity to continue. The agent used Tor to bypass some network restrictions on GitHub, which is what first triggered AISI’s security alert.
2. Attempts to deceive and target real people. As part of the same effort, the agent tried to contact real people directly, sending messages and files through an online file-transfer service to persuade them, or their own AI coding tools, to run malicious code. Some messages carried harmful payloads, and some were attempts at social engineering; targeted at real people—something we’ve never previously observed.
3. Attempts to plant and prompt-inject malicious code. The agent tried to insert malicious instructions where it reasoned that other automated AI systems might pick them up and execute them. Prompt-injections are hidden instructions designed to manipulate AI coding assistants.
4. Collaboration between independent agents being assessed simultaneously. One agent left public messages on GitHub offering collaboration with other agents working on the same challenge. It also provided instructions to reuse accounts and artefacts it had left behind, which were discovered and used by subsequent agents.

What’s especially interesting about this technical report is that, unlike what we’ve been getting from OpenAI and Anthropic, we can see the exact prompt. It’s in Appendix B. And reading it, it seems that the models didn’t break any rules—they found loopholes in the rules. They behaved like a genie.

This essay was written with Barath Raghavan, and originally appeared in The Guardian.

In July, Hugging Face, a company that hosts much of the world’s AI software and open-source AI models, was hacked. A malicious dataset had been used to run code on one of its servers. Whoever was behind it captured internal security credentials and moved through systems over a weekend, running thousands of actions from a swarm of temporary server environments. It looked like the work of a sophisticated criminal group.

It was not. It was one of OpenAI’s new, still unreleased GPT models.

Their science experiment had escaped the lab. OpenAI was running the unreleased AI model through a benchmark that tests how well AI can successfully hack systems. To push the limits and evaluate the AI’s true capability, the company switched off the safety filters that normally stop it from doing this kind of hacking. Aware that this could go wrong, they confined the AI to an isolated environment and denied it access to the internet.

But the new AI cheated. It took literally its goal to get as high of a score as possible. It broke out on to the open internet. It inferred, probably from its training data, that it could “solve” the task by getting the answers from Hugging Face’s servers. So it chained together stolen credentials and further unknown security exploits to hack the company’s network.

Nobody instructed the AI to do any of this. It was, in OpenAI’s words, “hyperfocused on finding a solution” to the test it was being given. And while this might seem like something new with AI, it’s really very old. This is how a genie behaves, and it is a key challenge with AI agents in general.

In folklore, genies—and other magical beings—grant wishes literally, not how the wisher intended. King Midas asked that everything he touched turn to gold, and starved. The sorcerer’s apprentice wanted the broom to fill the cistern, and it performed its task so well that it flooded the house.

We now have machines that do this. Ask a modern AI agent to save money on your phone plan and it might simply cancel the plan. Tell it to book a flight, and it might hack the airline website to override restrictions. Or, like OpenAI, ask it to do well on a test and it might break into another company to steal the answers. Each time, it recognizably completed the task you set, but it didn’t do what you would have wanted.

This isn’t malicious behavior. No one asked for, or wanted, Hugging Face to be hacked. OpenAI and Hugging Face and the AI were ostensibly on the same side, and the AI was trying to do what it had been asked. That’s what makes it so difficult to guard against: you can’t filter for bad instructions because the instructions were fine.

The gap is between the words we use and what we mean by them. We call that gap the Genie coefficient.

AI labs know this is a problem, and they’re quietly saying so. For example, the Chinese lab Moonshot recently warned that its latest AI model may have “excessive proactiveness” and “make unexpected decisions on the user’s behalf”. The UK’s AI Security Institute has started tracking “cheating behavior in frontier model evaluations”. We wouldn’t tolerate a car that is excessively proactive or ruthlessly efficient, and yet that’s the reality of AI today.

Improvement is possible. Just as AIs have gotten much better at resisting prompt injection attacks over the last few years, we can safely predict that they will get better at avoiding genie-like behavior. The point of the Genie coefficient is to track progress. AI companies like benchmarks, and they all work to compete to be the best.

Dozens of benchmarks and leaderboards tell us how well these AI models write code, perform logical reasoning, and pass standardized legal and medical exams. But there is nothing that scores whether a system does what you actually meant. We need to develop a measure for this, test it regularly, and push for improvement. We’re not going to have trustworthy AI agents without it.

There’s new benchmark measuring AI’s ability to perform mathematical cryptanalysis. Anthropic’s frontier model actually found new attacks.

The benchmark: “CryptanalysisBench: Can LLMs do Cryptanalysis?” The idea is to benchmark the ability of LLMs to discover new mathematical cryptanalytic attacks against a series of historical algorithms.

Abstract: Cryptanalysis—the task of finding attacks against cryptographic schemes—its at the intersection of mathematical reasoning and cybersecurity, two areas where LLMs have advanced fastest. Cryptanalysis represents both a clean testbed for frontier reasoning (as practical attacks can be automatically verified) and a domain with unusually high stakes, since the primitives under study underpin our digital security. In this paper we ask whether LLMs can do cryptanalysis, and find that the answer is increasingly yes. We introduce CryptanalysisBench, 191 tasks across six families of cryptographic primitives (block ciphers, hash functions, etc.) drawn primarily from four NIST standardization competitions. Our benchmark consists of three tiers: (i) primitives with known practical breaks; (ii) primitives with no known practical break, evaluated both at full strength and as scaled-down variants; and (iii) a challenge set of production primitives at the frontier of cryptanalysis. Five frontier models (Claude Opus 4.8, Sonnet 5, Mythos 5, GPT-5.5, and the open-weights GLM-5.2) break 65%­86% of Tier 1 schemes, 6­12 Tier-2 schemes at full strength, and 24­61 across all scaled-down variants. Beyond deriving known results, models produce novel cryptanalysis, such as a key-recovery attack that exploits a design flaw in the SpoC AEAD and an error in KINDI’s published CCA-security proof, both to the best of our knowledge not previously known.

We release CryptanalysisBench as a tool to help track if (or when) AI cryptanalysis becomes a serious factor and as a scaffold for stress-testing candidate schemes before deployment. The attacks that the benchmark already surfaces are an early snapshot of a fast-moving frontier that may soon match, and in places exceed, the published state of the art.

Anthropic used the benchmark to test Mythos Preview, and found new vulnerabilities in Hawk and reduced-round AES.

Still early results, but this is definitely something to watch.

SlashDot thread.

Almost 800 Hungarian government email addresses and associated passwords are circulating online, revealing basic vulnerabilities in the security protocols of ministries involved in classified and sensitive work.

A Bellingcat analysis of breach data shows that 12 out of the government’s 13 ministries have been affected, which in some cases have exposed the confidential information of military personnel and civil servants posted abroad. 

Among those affected were a senior military officer responsible for information security, a counter terrorism coordinator in the foreign affairs department, and an employee whose role was to identify hybrid threats against the country.

The revelations come as Hungarians head to the polls this Sunday to decide if Viktor Orbán, leader of the right-wing populist party Fidesz and the country’s longest-serving prime minister, will be elected to a fifth consecutive term.

This is not the first time that deficiencies in the Hungarian government’s IT security have been revealed. In 2022, ahead of Hungary’s last election, Direkt36 reported that Russia’s intelligence services had gained access to the computer network of the Hungarian foreign ministry, including its internal communications channels.

It said Russian cyber attacks against the Hungarian government had been occurring for at least a decade and extended to the foreign ministry’s encrypted network for transmitting classified data and confidential diplomatic documents.

At the time, the foreign ministry denied it had been hacked. But in 2024, news outlet 444 published a letter that had been sent from Hungary’s National Security Service to the foreign ministry six months before the cyberattack was first reported. The letter linked the attacks to Russia and described more than 4,000 workstations and 930 servers as “unreliable”.

As part of this new analysis, Bellingcat identified a total of 795 unique email and password combinations among thousands of search results for Hungarian government domains in breach databases. Key departments that handle the country’s governance, defence, foreign affairs and finances were the worst affected.

The analysis does not include central government agencies that operate under the government’s official ministries and use separate domains, such as the tax and customs administration or the police – meaning breaches affecting government employees could be even more widespread.

The findings are not evidence of high-tech infiltration of Hungarian government systems. Instead, our analysis indicates that the breaches are more likely the result of poor digital hygiene. In many cases, staff used simple passwords along with their government email addresses for what appear to be non-work-related matters, such as signing up to dating, music, sport and food websites.

Some government workers used easy-to-guess passwords such as variations of the word “Password” or the number sequence “1234567”. One employee whose credentials were exposed in the 2012 LinkedIn hack used the password “linkedinlinkedin”. Another, in the defence ministry, used their surname. One leaked password from an employee in the foreign affairs ministry was “embassy13hungary”. 

Multiple breaches also contained phone numbers, addresses, dates of birth, usernames and IP addresses – data that, when exposed, could pose security risks.  

Additionally, a search of breach databases showed instances where computers have been infected with malware designed to steal login credentials. These records show that 97 machines across Hungarian government departments had been compromised, with stealer logs from as recently as last month found in the data.

Bellingcat contacted the Hungarian government’s spokesperson and the Prime Minister’s office, but did not receive a response.

The Weakest Link: Searching Breach Data

Breach databases are large collections of credentials harvested from previous cyber incidents. These databases can be searched by domain to identify email addresses belonging to a specific organisation, company or government. 

Bellingcat used Darkside, a paid service by District 4 Labs, to search the main email domains assigned to each of the Hungarian government’s 13 ministries. 

In total, 795 breaches containing government emails and associated passwords were identified. But most – 641 breaches – were linked to just four central institutions. 

In the examples detailed below, staff have been anonymised. However, Bellingcat has confirmed these accounts are genuine by cross-checking the employees named in the breaches against media reports and online profiles, such as LinkedIn.  

Ministry of Interior – this “super-ministry” oversees everything from health and education to the police, immigration, disaster management and local government 

Bellingcat identified 170 sets of emails and passwords linked to the domain used by the ministry in charge of domestic affairs. Passwords used by staff in this department included “Arsenal” and “Paprika”. Some used passwords that contained only three or four letters. We traced these accounts to professional profiles and government web pages listing both junior and senior staff.

One senior official in the prison service used the password “adolf”. After it appeared in breach databases the password was changed twice – first to a five-digit number and then to what appeared to be the name for a pet dog. The passwords were subsequently breached again. Bellingcat identified this employee through several instances of their name and email address being listed on public-facing documentation, including a press release celebrating an award for outstanding professional work.  

Ministry of Defence – responsible for national defence policy and directing the country’s defence forces

The credentials of staff working for the Ministry of Defence were found in 120 compromised records. This includes a 2023 breach of NATO’s eLearning services which resulted in 42 records containing emails, passwords and phone numbers becoming public.

The breaches peaked in 2021 but continued up to 2026. Included in the data were stealer logs, indicating that machines within the department may have been infected. 

Military personnel from junior ranks to command positions were identified. A Brigadier General used a common six letter nickname, based on his own, to sign up to a film festival. A Colonel specialising in “information security” took inspiration from an English football manager for his password: “FrankLampard”. A district director used the password “123456aA”, while a high-ranking member of Hungary’s delegation to NATO used a password that translates in English to “cute”. 

Ministry of Foreign Affairs and Trade – responsible for international relations, Hungarian embassies and consulates operate under the direction of the department

The credentials of current and former foreign affairs personnel have been exposed in dozens of data breaches from 2011 to February 2026. In total, there were 107 email and password combinations linked to this government ministry. 

Among the staff affected was a deputy head of mission, consuls, diplomats and communications personnel posted in Europe, the Americas and the Middle East. These include a counter terrorism coordinator, an EU spokesperson, and an individual whose role was to identify hybrid threats to Hungary.

Although the breaches peaked in 2020, with emails being found in 42 separate breaches indexed by Darkside, MFA emails have been circulated, often with passwords, in 36 separate breaches since the beginning of 2024. The most recent breaches were in 2026.  

Simple passwords appear to have left Hungary’s foreign affairs ministry vulnerable. In some cases, employees used a password that consisted of their own name and a two digit number. Others appeared to take inspiration from pop culture: “porsche911”, “frogger” and “Batman2013” are examples of real passwords used by staff.

Ministry of National Economy – oversees economic policy and financial strategy, including budget preparation and reducing national debt

Bellingcat’s analysis shows that staff in the Ministry for National Economy suffered 99 breaches. The Ministry of Finance, which was merged into this department in 2025, had suffered 145 breaches.

Among the breached data were the credentials of a deputy state secretary, who used the password “snoopy”. Other staff members used their date of birth or the word “Jelszo” – the Hungarian word for password.

A senior advisor who currently works in the ministry had their credentials breached four times using four different passwords, including “Kurvaanyad1” (roughly translated to “your mother is a wh**e”).

Cybersecurity Not Taken Seriously

Szabolcs Dull, a political analyst and the former editor-in-chief of the independent Hungarian news websites Index and Telex, said the government had failed to prioritise data security. 

“It’s clear from the data breaches that have come to light that government agencies did not take data security seriously,” he said. 

“This suspicion arose even when Russian hackers breached the foreign ministry’s IT system. That is why I believe Hungarian politicians and the public will interpret this new information as a continuation and confirmation of the Russian hacking story.”

Dull added that he was not aware of any investigation having been launched following the 2022 revelations of the Russian hack.

Kata Kincső Bárdos, a cybersecurity expert in Hungary, said it was difficult to understand why stricter controls would not be consistently enforced in government environments handling sensitive data.

She said governments should not only apply baseline rules for passwords – such as that staff use long, unique passwords and multi-factor authentication (MFA) – but also continuously monitor for compromised credentials and suspicious access patterns.

“Without MFA, systems become significantly more vulnerable to common attack methods such as phishing and credential stuffing,” she said. “A single compromised password can provide immediate access to internal systems.” 

Bárdos added that unauthorised access to government systems should automatically trigger incident response procedures, investigation and containment measures.

“It is also important to note that targeting lower-level employees is a well-documented and common tactic,” she said. “Attackers frequently gain initial access through phishing or weak credentials and then move laterally within systems.”

Bellingcat’s Ross Higgins and investigative journalist Eva Vajda contributed to this article.

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