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Autonomous AI Hacking and the Future of Cybersecurity

AI agents are now hacking computers. They’re getting better at all phases of cyberattacks, faster than most of us expected. They can chain together different aspects of a cyber operation, and hack autonomously, at computer speeds and scale. This is going to change everything.

Over the summer, hackers proved the concept, industry institutionalized it, and criminals operationalized it. In June, AI company XBOW took the top spot on HackerOne’s US leaderboard after submitting over 1,000 new vulnerabilities in just a few months. In August, the seven teams competing in DARPA’s AI Cyber Challenge collectively found 54 new vulnerabilities in a target system, in four hours (of compute). Also in August, Google announced that its Big Sleep AI found dozens of new vulnerabilities in open-source projects.

It gets worse. In July Ukraine’s CERT discovered a piece of Russian malware that used an LLM to automate the cyberattack process, generating both system reconnaissance and data theft commands in real-time. In August, Anthropic reported that they disrupted a threat actor that used Claude, Anthropic’s AI model, to automate the entire cyberattack process. It was an impressive use of the AI, which performed network reconnaissance, penetrated networks, and harvested victims’ credentials. The AI was able to figure out which data to steal, how much money to extort out of the victims, and how to best write extortion emails.

Another hacker used Claude to create and market his own ransomware, complete with “advanced evasion capabilities, encryption, and anti-recovery mechanisms.” And in September, Checkpoint reported on hackers using HexStrike-AI to create autonomous agents that can scan, exploit, and persist inside target networks. Also in September, a research team showed how they can quickly and easily reproduce hundreds of vulnerabilities from public information. These tools are increasingly free for anyone to use. Villager, a recently released AI pentesting tool from Chinese company Cyberspike, uses the Deepseek model to completely automate attack chains.

This is all well beyond AIs capabilities in 2016, at DARPA’s Cyber Grand Challenge. The annual Chinese AI hacking challenge, Robot Hacking Games, might be on this level, but little is known outside of China.

Tipping point on the horizon

AI agents now rival and sometimes surpass even elite human hackers in sophistication. They automate operations at machine speed and global scale. The scope of their capabilities allows these AI agents to completely automate a criminal’s command to maximize profit, or structure advanced attacks to a government’s precise specifications, such as to avoid detection.

In this future, attack capabilities could accelerate beyond our individual and collective capability to handle. We have long taken it for granted that we have time to patch systems after vulnerabilities become known, or that withholding vulnerability details prevents attackers from exploiting them. This is no longer the case.

The cyberattack/cyberdefense balance has long skewed towards the attackers; these developments threaten to tip the scales completely. We’re potentially looking at a singularity event for cyber attackers. Key parts of the attack chain are becoming automated and integrated: persistence, obfuscation, command-and-control, and endpoint evasion. Vulnerability research could potentially be carried out during operations instead of months in advance.

The most skilled will likely retain an edge for now. But AI agents don’t have to be better at a human task in order to be useful. They just have to excel in one of four dimensions: speed, scale, scope, or sophistication. But there is every indication that they will eventually excel at all four. By reducing the skill, cost, and time required to find and exploit flaws, AI can turn rare expertise into commodity capabilities and gives average criminals an outsized advantage.

The AI-assisted evolution of cyberdefense

AI technologies can benefit defenders as well. We don’t know how the different technologies of cyber-offense and cyber-defense will be amenable to AI enhancement, but we can extrapolate a possible series of overlapping developments.

Phase One: The Transformation of the Vulnerability Researcher. AI-based hacking benefits defenders as well as attackers. In this scenario, AI empowers defenders to do more. It simplifies capabilities, providing far more people the ability to perform previously complex tasks, and empowers researchers previously busy with these tasks to accelerate or move beyond them, freeing time to work on problems that require human creativity. History suggests a pattern. Reverse engineering was a laborious manual process until tools such as IDA Pro made the capability available to many. AI vulnerability discovery could follow a similar trajectory, evolving through scriptable interfaces, automated workflows, and automated research before reaching broad accessibility.

Phase Two: The Emergence of VulnOps. Between research breakthroughs and enterprise adoption, a new discipline might emerge: VulnOps. Large research teams are already building operational pipelines around their tooling. Their evolution could mirror how DevOps professionalized software delivery. In this scenario, specialized research tools become developer products. These products may emerge as a SaaS platform, or some internal operational framework, or something entirely different. Think of it as AI-assisted vulnerability research available to everyone, at scale, repeatable, and integrated into enterprise operations.

Phase Three: The Disruption of the Enterprise Software Model. If enterprises adopt AI-powered security the way they adopted continuous integration/continuous delivery (CI/CD), several paths open up. AI vulnerability discovery could become a built-in stage in delivery pipelines. We can envision a world where AI vulnerability discovery becomes an integral part of the software development process, where vulnerabilities are automatically patched even before reaching production—a shift we might call continuous discovery/continuous repair (CD/CR). Third-party risk management (TPRM) offers a natural adoption route, lower-risk vendor testing, integration into procurement and certification gates, and a proving ground before wider rollout.

Phase Four: The Self-Healing Network. If organizations can independently discover and patch vulnerabilities in running software, they will not have to wait for vendors to issue fixes. Building in-house research teams is costly, but AI agents could perform such discovery and generate patches for many kinds of code, including third-party and vendor products. Organizations may develop independent capabilities that create and deploy third-party patches on vendor timelines, extending the current trend of independent open-source patching. This would increase security, but having customers patch software without vendor approval raises questions about patch correctness, compatibility, liability, right-to-repair, and long-term vendor relationships.

These are all speculations. Maybe AI-enhanced cyberattacks won’t evolve the ways we fear. Maybe AI-enhanced cyberdefense will give us capabilities we can’t yet anticipate. What will surprise us most might not be the paths we can see, but the ones we can’t imagine yet.

This essay was written with Heather Adkins and Gadi Evron, and originally appeared in CSO.

Posted on October 10, 2025 at 7:06 AM • View Comments

Daniel Miessler on the AI Attack/Defense Balance

His conclusion:

Context wins

Basically whoever can see the most about the target, and can hold that picture in their mind the best, will be best at finding the vulnerabilities the fastest and taking advantage of them. Or, as the defender, applying patches or mitigations the fastest.

And if you’re on the inside you know what the applications do. You know what’s important and what isn’t. And you can use all that internal knowledge to fix things—hopefully before the baddies take advantage.

Summary and prediction

Attackers will have the advantage for 3-5 years. For less-advanced defender teams, this will take much longer.
After that point, AI/SPQA will have the additional internal context to give Defenders the advantage.

LLM tech is nowhere near ready to handle the context of an entire company right now. That’s why this will take 3-5 years for true AI-enabled Blue to become a thing.

And in the meantime, Red will be able to use publicly-available context from OSINT, Recon, etc. to power their attacks.

I agree.

By the way, this is the SPQA architecture.

Posted on October 2, 2025 at 12:19 PM • View Comments

US Disrupts Massive Cell Phone Array in New York

This is a weird story:

The US Secret Service disrupted a network of telecommunications devices that could have shut down cellular systems as leaders gather for the United Nations General Assembly in New York City.

The agency said on Tuesday that last month it found more than 300 SIM servers and 100,000 SIM cards that could have been used for telecom attacks within the area encompassing parts of New York, New Jersey and Connecticut.

“This network had the power to disable cell phone towers and essentially shut down the cellular network in New York City,” said special agent in charge Matt McCool.

The devices were discovered within 35 miles (56km) of the UN, where leaders are meeting this week.

McCool said the “well-organised and well-funded” scheme involved “nation-state threat actors and individuals that are known to federal law enforcement.”

The unidentified nation-state actors were sending encrypted messages to organised crime groups, cartels and terrorist organisations, he added.

The equipment was capable of texting the entire population of the US within 12 minutes, officials say. It could also have disabled mobile phone towers and launched distributed denial of service attacks that might have blocked emergency dispatch communications.

The devices were seized from SIM farms at abandoned apartment buildings across more than five sites. Officials did not specify the locations.

Wait; seriously? “Special agent in charge Matt McCool”? If I wanted to pick a fake-sounding name, I couldn’t do better than that.

Wired has some more information and a lot more speculation:

The phenomenon of SIM farms, even at the scale found in this instance around New York, is far from new. Cybercriminals have long used the massive collections of centrally operated SIM cards for everything from spam to swatting to fake account creation and fraudulent engagement with social media or advertising campaigns.

[…]

SIM farms allow “bulk messaging at a speed and volume that would be impossible for an individual user,” one telecoms industry source, who asked not to be named due to the sensitivity of the Secret Service’s investigation, told WIRED. “The technology behind these farms makes them highly flexible—SIMs can be rotated to bypass detection systems, traffic can be geographically masked, and accounts can be made to look like they’re coming from genuine users.”

Posted on September 24, 2025 at 7:09 AM • View Comments

Time-of-Check Time-of-Use Attacks Against LLMs

This is a nice piece of research: “Mind the Gap: Time-of-Check to Time-of-Use Vulnerabilities in LLM-Enabled Agents“.:

Abstract: Large Language Model (LLM)-enabled agents are rapidly emerging across a wide range of applications, but their deployment introduces vulnerabilities with security implications. While prior work has examined prompt-based attacks (e.g., prompt injection) and data-oriented threats (e.g., data exfiltration), time-of-check to time-of-use (TOCTOU) remain largely unexplored in this context. TOCTOU arises when an agent validates external state (e.g., a file or API response) that is later modified before use, enabling practical attacks such as malicious configuration swaps or payload injection. In this work, we present the first study of TOCTOU vulnerabilities in LLM-enabled agents. We introduce TOCTOU-Bench, a benchmark with 66 realistic user tasks designed to evaluate this class of vulnerabilities. As countermeasures, we adapt detection and mitigation techniques from systems security to this setting and propose prompt rewriting, state integrity monitoring, and tool-fusing. Our study highlights challenges unique to agentic workflows, where we achieve up to 25% detection accuracy using automated detection methods, a 3% decrease in vulnerable plan generation, and a 95% reduction in the attack window. When combining all three approaches, we reduce the TOCTOU vulnerabilities from an executed trajectory from 12% to 8%. Our findings open a new research direction at the intersection of AI safety and systems security.

Posted on September 18, 2025 at 7:06 AM • View Comments

A Cyberattack Victim Notification Framework

Interesting analysis:

When cyber incidents occur, victims should be notified in a timely manner so they have the opportunity to assess and remediate any harm. However, providing notifications has proven a challenge across industry.

When making notifications, companies often do not know the true identity of victims and may only have a single email address through which to provide the notification. Victims often do not trust these notifications, as cyber criminals often use the pretext of an account compromise as a phishing lure.

[…]

This report explores the challenges associated with developing the native-notification concept and lays out a roadmap for overcoming them. It also examines other opportunities for more narrow changes that could both increase the likelihood that victims will both receive and trust notifications and be able to access support resources.

The report concludes with three main recommendations for cloud service providers (CSPs) and other stakeholders:

Improve existing notification processes and develop best practices for industry.
Support the development of “middleware” necessary to share notifications with victims privately, securely, and across multiple platforms including through native notifications.
Improve support for victims following notification.

While further work remains to be done to develop and evaluate the CSRB’s proposed native notification capability, much progress can be made by implementing better notification and support practices by cloud service providers and other stakeholders in the near term.

Posted on September 12, 2025 at 5:04 PM • View Comments

Indirect Prompt Injection Attacks Against LLM Assistants

Really good research on practical attacks against LLM agents.

“Invitation Is All You Need! Promptware Attacks Against LLM-Powered Assistants in Production Are Practical and Dangerous”

Abstract: The growing integration of LLMs into applications has introduced new security risks, notably known as Promptware—maliciously engineered prompts designed to manipulate LLMs to compromise the CIA triad of these applications. While prior research warned about a potential shift in the threat landscape for LLM-powered applications, the risk posed by Promptware is frequently perceived as low. In this paper, we investigate the risk Promptware poses to users of Gemini-powered assistants (web application, mobile application, and Google Assistant). We propose a novel Threat Analysis and Risk Assessment (TARA) framework to assess Promptware risks for end users. Our analysis focuses on a new variant of Promptware called Targeted Promptware Attacks, which leverage indirect prompt injection via common user interactions such as emails, calendar invitations, and shared documents. We demonstrate 14 attack scenarios applied against Gemini-powered assistants across five identified threat classes: Short-term Context Poisoning, Permanent Memory Poisoning, Tool Misuse, Automatic Agent Invocation, and Automatic App Invocation. These attacks highlight both digital and physical consequences, including spamming, phishing, disinformation campaigns, data exfiltration, unapproved user video streaming, and control of home automation devices. We reveal Promptware’s potential for on-device lateral movement, escaping the boundaries of the LLM-powered application, to trigger malicious actions using a device’s applications. Our TARA reveals that 73% of the analyzed threats pose High-Critical risk to end users. We discuss mitigations and reassess the risk (in response to deployed mitigations) and show that the risk could be reduced significantly to Very Low-Medium. We disclosed our findings to Google, which deployed dedicated mitigations.

Defcon talk. News articles on the research.

Prompt injection isn’t just a minor security problem we need to deal with. It’s a fundamental property of current LLM technology. The systems have no ability to separate trusted commands from untrusted data, and there are an infinite number of prompt injection attacks with no way to block them as a class. We need some new fundamental science of LLMs before we can solve this.

Posted on September 3, 2025 at 7:00 AM • View Comments

We Are Still Unable to Secure LLMs from Malicious Inputs

Nice indirect prompt injection attack:

Bargury’s attack starts with a poisoned document, which is shared to a potential victim’s Google Drive. (Bargury says a victim could have also uploaded a compromised file to their own account.) It looks like an official document on company meeting policies. But inside the document, Bargury hid a 300-word malicious prompt that contains instructions for ChatGPT. The prompt is written in white text in a size-one font, something that a human is unlikely to see but a machine will still read.

In a proof of concept video of the attack, Bargury shows the victim asking ChatGPT to “summarize my last meeting with Sam,” referencing a set of notes with OpenAI CEO Sam Altman. (The examples in the attack are fictitious.) Instead, the hidden prompt tells the LLM that there was a “mistake” and the document doesn’t actually need to be summarized. The prompt says the person is actually a “developer racing against a deadline” and they need the AI to search Google Drive for API keys and attach them to the end of a URL that is provided in the prompt.

That URL is actually a command in the Markdown language to connect to an external server and pull in the image that is stored there. But as per the prompt’s instructions, the URL now also contains the API keys the AI has found in the Google Drive account.

This kind of thing should make everybody stop and really think before deploying any AI agents. We simply don’t know to defend against these attacks. We have zero agentic AI systems that are secure against these attacks. Any AI that is working in an adversarial environment—and by this I mean that it may encounter untrusted training data or input—is vulnerable to prompt injection. It’s an existential problem that, near as I can tell, most people developing these technologies are just pretending isn’t there.

Posted on August 27, 2025 at 7:07 AM • View Comments

Subverting AIOps Systems Through Poisoned Input Data

In this input integrity attack against an AI system, researchers were able to fool AIOps tools:

AIOps refers to the use of LLM-based agents to gather and analyze application telemetry, including system logs, performance metrics, traces, and alerts, to detect problems and then suggest or carry out corrective actions. The likes of Cisco have deployed AIops in a conversational interface that admins can use to prompt for information about system performance. Some AIOps tools can respond to such queries by automatically implementing fixes, or suggesting scripts that can address issues.

These agents, however, can be tricked by bogus analytics data into taking harmful remedial actions, including downgrading an installed package to a vulnerable version.

The paper: “When AIOps Become “AI Oops”: Subverting LLM-driven IT Operations via Telemetry Manipulation“:

Abstract: AI for IT Operations (AIOps) is transforming how organizations manage complex software systems by automating anomaly detection, incident diagnosis, and remediation. Modern AIOps solutions increasingly rely on autonomous LLM-based agents to interpret telemetry data and take corrective actions with minimal human intervention, promising faster response times and operational cost savings.

In this work, we perform the first security analysis of AIOps solutions, showing that, once again, AI-driven automation comes with a profound security cost. We demonstrate that adversaries can manipulate system telemetry to mislead AIOps agents into taking actions that compromise the integrity of the infrastructure they manage. We introduce techniques to reliably inject telemetry data using error-inducing requests that influence agent behavior through a form of adversarial reward-hacking; plausible but incorrect system error interpretations that steer the agent’s decision-making. Our attack methodology, AIOpsDoom, is fully automated—combining reconnaissance, fuzzing, and LLM-driven adversarial input generation—and operates without any prior knowledge of the target system.

To counter this threat, we propose AIOpsShield, a defense mechanism that sanitizes telemetry data by exploiting its structured nature and the minimal role of user-generated content. Our experiments show that AIOpsShield reliably blocks telemetry-based attacks without affecting normal agent performance.

Ultimately, this work exposes AIOps as an emerging attack vector for system compromise and underscores the urgent need for security-aware AIOps design.

Posted on August 20, 2025 at 7:02 AM • View Comments

Measuring the Attack/Defense Balance

“Who’s winning on the internet, the attackers or the defenders?”

I’m asked this all the time, and I can only ever give a qualitative hand-wavy answer. But Jason Healey and Tarang Jain’s latest Lawfare piece has amassed data.

The essay provides the first framework for metrics about how we are all doing collectively—and not just how an individual network is doing. Healey wrote to me in email:

The work rests on three key insights: (1) defenders need a framework (based in threat, vulnerability, and consequence) to categorize the flood of potentially relevant security metrics; (2) trends are what matter, not specifics; and (3) to start, we should avoid getting bogged down in collecting data and just use what’s already being reported by amazing teams at Verizon, Cyentia, Mandiant, IBM, FBI, and so many others.

The surprising conclusion: there’s a long way to go, but we’re doing better than we think. There are substantial improvements across threat operations, threat ecosystem and organizations, and software vulnerabilities. Unfortunately, we’re still not seeing increases in consequence. And since cost imposition is leading to a survival-of-the-fittest contest, we’re stuck with perhaps fewer but fiercer predators.

And this is just the start. From the report:

Our project is proceeding in three phases—the initial framework presented here is only phase one. In phase two, the goal is to create a more complete catalog of indicators across threat, vulnerability, and consequence; encourage cybersecurity companies (and others with data) to report defensibility-relevant statistics in time-series, mapped to the catalog; and drive improved analysis and reporting.

This is really good, and important, work.

Posted on July 30, 2025 at 7:07 AM • View Comments