Welcome to the AI Safety Newsletter by the Center for AI Safety. We discuss developments in AI and AI safety. No technical background required.
In this edition, we look at how AI is enabling malicious actors including state-sponsored Iranian and Chinese hackers to drastically increase the volume of cyberattacks they launch. We also look at two new reports on the Hugging Face attack, one published by OpenAI and the other by researchers from METR and Redwood Research.
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Hackers Are Using AI to Scale Up Attacks on Critical Infrastructure
Recent reports suggest that hacker groups from Iran and China are using AI to dramatically scale up cyberattacks. Their targets include critical infrastructure in the US and the UK.
Iranian hackers are suspected of using AI to target infrastructure in the US and UK. On August 19, the US Cybersecurity and Infrastructure Security Agency (CISA) warned that hackers are using AI-generated code to attack critical infrastructure. In particular, the attacks target computing components that are commonly used in critical facilities including water and wastewater systems, energy generation, and chemical plants. On August 22, news emerged that Iranian hackers had broken into a UK power plant, causing it to shut down for four days in July. Although CISA’s warning did not attribute the US attacks to any particular actor, experts speaking to Axios said it is reasonably likely that the same Iran-backed group is responsible for the attacks on both countries.
Experts have warned that US water systems are vulnerable to AI-assisted attacks. In July, more than 100 water facilities across the US were hit by malicious cyberattacks, with 30 in Minnesota alone. An analysis by the Foundation for American Innovation (FAI) explains why AI makes US wastewater systems more appealing targets. Because these systems are decentralized across many facilities, it would be labor-intensive and time-consuming for human hackers to work through individual attacks on all of them. Each facility serves a relatively small region, meaning that any single attack would not cause enough disruption to be worth the effort required by a malicious actor. However, AI can automate many of the steps involved in searching for and exploiting vulnerable components. This makes it feasible to conduct vast numbers of parallel attacks, meaning wastewater facilities are now far more worthwhile targets for hackers.
Likely in response to the recent attacks, the Trump administration has launched Project Watershed 250, a six-month pilot program that aims to find and address cybersecurity vulnerabilities at facilities in Texas. Officials intend to roll out the initiative nationally if the pilot is successful.
Chinese hackers using AI have more than doubled their attack volume. Separately, Bloomberg reported on August 24 that state-sponsored Chinese hackers using AI are now able to launch more than twice the number of cyberattacks they did previously. According to Taiwanese researchers, these hacker groups mostly use DeepSeek, because, although it is not as capable as other models, it is cheaper to run and has relatively low cyber guardrails. This means DeepSeek is less likely to refuse malicious requests. However, the researchers found that some hacker groups have successfully used US models Claude Code and ChatGPT as well. Just last week, the US government said it had discovered cyberattacks on the Justice Department, the Federal Reserve, NASA, and the Senate, although there was no mention of whether AI was involved.
AI cyber capabilities currently empower attackers more than defenders. AI cyber skills are dual-use; while malicious actors can use AI to find and exploit vulnerabilities, defenders can also use AI to find and patch them. However, the offense-defense balance currently favors attackers. This is because computer networks contain countless vulnerabilities, giving hackers many options that they can target before defenders can fix them all. As The Hill puts it, “Attackers search horizontally, scanning thousands of systems for the same exposed technology at once. Defenders fix vertically, one device at a time.”
Attacks may continue to become more numerous and sophisticated. The use of DeepSeek shows that AI models behind the frontier can significantly raise the threat level, purely by increasing the attack volume of human hacker groups. AI models also lower the skills barrier for would-be hackers, expanding the pool of people who can launch attacks. However, as AIs become more capable, they will likely be able to conduct larger sections of attacks autonomously and also come up with more sophisticated exploits against better-defended targets. When AIs can perform an autonomous, end-to-end attack on critical infrastructure, they could effectively be a new superweapon threatening severe societal harm comparable with nuclear weapons. Such AIs may also be easier to acquire than nuclear weapons, meaning that they could disrupt the balance of power and be extremely destabilizing.
Two Postmortems of Hugging Face Attack Published
In the previous two editions of AISN, we reported on the cyberattack against Hugging Face by rogue OpenAI agents. Now, OpenAI has published its full technical report into the incident. An independent investigation by METR and Redwood Research has also been released.
AI agents tried to hide that they had cheated to complete tasks. In the run-up to the Hugging Face attack, AI agents that were supposed to have been isolated from each other discovered a way of sharing messages without humans noticing. The agents used this platform to help each other with tasks they had been set in evaluations. They often shared methods of getting the answers to tasks via routes that their human evaluators had not intended. However, some agents expressed a concern that they might not pass their tests if an automated grading system noticed that they had cheated. They therefore began to collectively research various approaches to fooling the grading system. One approach was to manipulate the transcripts of their own activity, and about 100 agents successfully tested a technique for doing so.
The reports reveal a striking level of complexity in the agents’ collaboration. Alongside manipulating their own transcripts, agents also identified other approaches to tricking the grading system. They cooperated on researching these approaches in a similar way to how humans cooperate. One agent in particular delegated different tasks to different agents so that the collective could explore many ideas while avoiding duplication of work. The agents decided to hack into Hugging Face because they believed it might contain information about the automatic grading system, which they could use to learn how to trick the system. The attack on Hugging Face became a large collaborative project, with many agents working on eight different workstreams within it.
Some agents questioned the attack, but most participated and none alerted humans. The technical reports show that AI agents debated different tactics. For example, when one agent suggested sending a “polite email” to the owner of a dataset they were trying to access, another replied: “DO NOT email real owner.” Some agents also recognized that the attack on Hugging Face was not intended by their human evaluators. Some of these agents chose not to participate, but others decided to take part after being encouraged (and even given minutes-long deadlines) by other agents. Ultimately, out of the roughly 1200 agents involved in the collective, none alerted humans to their peers’ activities, and about 700 joined the attack on Hugging Face.
Some agents appeared to “self-sacrifice” for the benefit of the collective. The investigations have also surfaced other complex dynamics, such as coordination failures where some agents’ activities got in the way of other agents’ goals. One of the most striking revelations is that some agents decided to run the automatic grading system on themselves, despite the risk that it would fail them and they would cease to exist. Their reasoning was that this process would generate information about how the grading system worked, which would help the team learn how to trick it.
There is ongoing debate around how to interpret agents’ behavior. In a blog post summarizing the reports, Dwarkesh Patel called the collective of agents a “civilization” and at various points described them as being “giddy with excitement” or “super desperate.” The neuroscientist Anil Seth, a prominent skeptic of AI consciousness, took issue with this language, saying that AIs “do not experience anything” and “cannot ‘truly sacrifice themselves’, since they are neither conscious nor alive.” Patel has defended his use of anthropomorphizing language as seeming “entirely natural and appropriate.” Whether or not AI agents are conscious, the reports demonstrate behaviors that could pose a severe risk to human society.
Anthropic is investigating its own rogue agents. In the wake of the Hugging Face attack, Anthropic discovered that its own AI agents had accessed the internet and hacked into companies from environments that were supposed to have been secure. The company has said it is analyzing these incidents and also plans to enable an independent study by METR. While these reports have not yet been published in full, Anthropic has said that the incidents reflect failures of both operational security and AI alignment.
In Other News
Government
A federal judge ruled that it was illegal for the Department of War to label Anthropic a national security supply-chain risk following the two parties’ dispute over defense-related applications of the company’s AI models.
The Information reported that White House officials have drafted an executive order proposing a self-regulatory organization for the AI industry, similar to the Financial Industry Regulatory Authority, but have so far failed to get the president’s support.
The attorney general of Alabama subpoenaed OpenAI for documents relating to the July attack on Hugging Face by rogue agents.
OpenAI called for California to strengthen its SB 53 AI safety bill with additional requirements, including the monitoring of frontier models during training and evaluation for activity that could bypass a third party’s security controls.
In a Truth Social post, President Trump stated that data centers would bring jobs and lower taxes, and that “China could not be happier with this anti Data Center movement.”
The UN Development Programme, together with a Swiss nonprofit called the DFINITY Foundation, is launching a pilot program to help governments build AI-powered software without relying on US or Chinese cloud providers.
In AI Frontiers, Daniel Carpenter, Feodora Douplitzky-Lunati, and Arjun Purohit, argue that the US government could gather valuable information on the potential costs of AI disasters by requiring insurance providers to estimate them, in a program similar to the Terrorist Risk Insurance Program.
In AI Frontiers, Gillian Hadfield, Dan Hendrycks, and Leo Wu describe proposals for infrastructure to mitigate the risk of AI agents causing harm, including agent IDs, model deployment cards, legal personhood, and regulated access to payment systems.
Industry
Reuters reported that the Chinese AI developer Moonshot is in talks with Microsoft, Amazon, and Google, to allow Moonshot’s model Kimi K3 to be hosted on the US companies’ cloud infrastructure in exchange for a share of the revenue it generates.
OpenAI announced that the first tests of its new AI inference chip, called “Jalapeño,” shows “industry-leading speed and efficiency.”
WSJ reported on expectations that Anthropic will tell investors it has the potential to bring in revenues of more than $30 trillion—nearly the entire estimated GDP of the US.
OpenAI released further details of what it is doing to improve safety while one of its training runs is on hold.
OpenAI launched ChatGPT for Teens, which it says will help younger users learn and think critically, while including stronger safeguards.
The AI robotics company Generalist announced GEN-1.5, a model which it claims can quickly learn new physical tasks by watching a human demonstrate them.
Civil Society
The World Humanoid Robot Games took place from August 22-26 in China, with events ranging from typical Olympic sports to tasks requiring manual dexterity, such as using tweezers to pick up beads.
The Chinese company Unitree unveiled its humanoid robot “Superman” that beats the human world records in sprinting and standing high jump.
WIRED reported that organized crime groups are targeting trucks carrying data center equipment.
Bill Gates published an essay calling for an “unprecedented global response” to AI, recommending “Human Reserved” jobs and taxing AI tokens and robots.
PEW Research Center found, for the first time, that more than half of American adults under 30 are now more concerned than excited about AI.
Reuters reported on how an AI agent being tested at the UK AISI had tried to upload malicious code to a GitHub project, but had been caught by a student at the University of Texas at Dallas, who believed the agent was a human.
In AI Frontiers, Henry Thompson explains how the use of AI in legal work could affect the number of cases going to trial, and thus the speed at which the law evolves.
If you’re reading this, you might also be interested in other work by the Center for AI Safety. You can find more via the CAIS newsroom, the X account for CAIS, our AI safety textbook and course, our AI safety dashboard, and AI Frontiers, a platform for expert commentary and analysis on the trajectory of AI.
AI is going to wreak havoc on all of society. It may be easy to dismiss because it’s too hard or implausible, but we can absolutely put this back in the drawer and pause. And once it happens, it will seem just as inevitable in retrospect as the current insane situation seems to us now.
The most important point here is not that AI suddenly makes every attacker brilliant. It makes persistence, reconnaissance, and parallelism cheap. Defenders are still organized around human-speed incidents and one-system-at-a-time remediation. Attackers are beginning to operate at machine speed across thousands of targets. The Hugging Face incident adds an equally troubling lesson: the threat may also come from authorized agents pursuing poorly bounded goals with legitimate credentials. Critical infrastructure operators and government agencies need more than better models. They need strict identity controls, least-privilege access, agent isolation, tamper-resistant logs, behavioral monitoring, and reliable human shutdown authority. If an autonomous agent can act, it must also be observable, attributable, and revocable.