Entries Tagged "LLM"

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Will AI Strengthen or Undermine Democracy?

Listen to the Audio on NextBigIdeaClub.com

Below, co-authors Bruce Schneier and Nathan E. Sanders share five key insights from their new book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship.

What’s the big idea?

AI can be used both for and against the public interest within democracies. It is already being used in the governing of nations around the world, and there is no escaping its continued use in the future by leaders, policy makers, and legal enforcers. How we wire AI into democracy today will determine if it becomes a tool of oppression or empowerment.

1. AI’s global democratic impact is already profound.

It’s been just a few years since ChatGPT stormed into view and AI’s influence has already permeated every democratic process in governments around the world:

  • In 2022, an artist collective in Denmark founded the world’s first political party committed to an AI-generated policy platform.
  • Also in 2022, South Korean politicians running for the presidency were the first to use AI avatars to communicate with voters en masse.
  • In 2023, a Brazilian municipal legislator passed the first enacted law written by AI.
  • In 2024, a U.S. federal court judge started using AI to interpret the plain meaning of words in U.S. law.
  • Also in 2024, the Biden administration disclosed more than two thousand discrete use cases for AI across the agencies of the U.S. federal government.

The examples illustrate the diverse uses of AI across citizenship, politics, legislation, the judiciary, and executive administration.

Not all of these uses will create lasting change. Some of these will be one-offs. Some are inherently small in scale. Some were publicity stunts. But each use case speaks to a shifting balance of supply and demand that AI will increasingly mediate.

Legislators need assistance drafting bills and have limited staff resources, especially at the local and state level. Historically, they have looked to lobbyists and interest groups for help. Increasingly, it’s just as easy for them to use an AI tool.

2. The first places AI will be used are where there is the least public oversight.

Many of the use cases for AI in governance and politics have vocal objectors. Some make us uncomfortable, especially in the hands of authoritarians or ideological extremists.

In some cases, politics will be a regulating force to prevent dangerous uses of AI. Massachusetts has banned the use of AI face recognition in law enforcement because of real concerns voiced by the public about their tendency to encode systems of racial bias.

Some of the uses we think might be most impactful are unlikely to be adopted fast because of legitimate concern about their potential to make mistakes, introduce bias, or subvert human agency. AIs could be assistive tools for citizens, acting as their voting proxies to help us weigh in on larger numbers of more complex ballot initiatives, but we know that many will object to anything that verges on AIs being given a vote.

But AI will continue to be rapidly adopted in some aspects of democracy, regardless of how the public feels. People within democracies, even those in government jobs, often have great independence. They don’t have to ask anyone if it’s ok to use AI, and they will use it if they see that it benefits them. The Brazilian city councilor who used AI to draft a bill did not ask for anyone’s permission. The U.S. federal judge who used AI to help him interpret law did not have to check with anyone first. And the Trump administration seems to be using AI for everything from drafting tariff policies to writing public health reports—with some obvious drawbacks.

It’s likely that even the thousands of disclosed AI uses in government are only the tip of the iceberg. These are just the applications that governments have seen fit to share; the ones they think are the best vetted, most likely to persist, or maybe the least controversial to disclose.

3. Elites and authoritarians will use AI to concentrate power.

Many Westerners point to China as a cautionary tale of how AI could empower autocracy, but the reality is that AI provides structural advantages to entrenched power in democratic governments, too. The nature of automation is that it gives those at the top of a power structure more control over the actions taken at its lower levels.

It’s famously hard for newly elected leaders to exert their will over the many layers of human bureaucracies. The civil service is large, unwieldy, and messy. But it’s trivial for an executive to change the parameters and instructions of an AI model being used to automate the systems of government.

The dynamic of AI effectuating concentration of power extends beyond government agencies. Over the past five years, Ohio has undertaken a project to do a wholesale revision of its administrative code using AI. The leaders of that project framed it in terms of efficiency and good governance: deleting millions of words of outdated, unnecessary, or redundant language. The same technology could be applied to advance more ideological ends, like purging all statutory language that places burdens on business, neglects to hold businesses accountable, protects some class of people, or fails to protect others.

Whether you like or despise automating the enactment of those policies will depend on whether you stand with or are opposed to those in power, and that’s the point. AI gives any faction with power the potential to exert more control over the levers of government.

4. Organizers will find ways to use AI to distribute power instead.

We don’t have to resign ourselves to a world where AI makes the rich richer and the elite more powerful. This is a technology that can also be wielded by outsiders to help level the playing field.

In politics, AI gives upstart and local candidates access to skills and the ability to do work on a scale that used to only be available to well-funded campaigns. In the 2024 cycle, Congressional candidates running against incumbents like Glenn Cook in Georgia and Shamaine Daniels in Pennsylvania used AI to help themselves be everywhere all at once. They used AI to make personalized robocalls to voters, write frequent blog posts, and even generate podcasts in the candidate’s voice. In Japan, a candidate for Governor of Tokyo used an AI avatar to respond to more than eight thousand online questions from voters.

Outside of public politics, labor organizers are also leveraging AI to build power. The Worker’s Lab is a U.S. nonprofit developing assistive technologies for labor unions, like AI-enabled apps that help service workers report workplace safety violations. The 2023 Writers’ Guild of America strike serves as a blueprint for organizers. They won concessions from Hollywood studios that protect their members against being displaced by AI while also winning them guarantees for being able to use AI as assistive tools to their own benefit.

5. The ultimate democratic impact of AI depends on us.

If you are excited about AI and see the potential for it to make life, and maybe even democracy, better around the world, recognize that there are a lot of people who don’t feel the same way.

If you are disturbed about the ways you see AI being used and worried about the future that leads to, recognize that the trajectory we’re on now is not the only one available.

The technology of AI itself does not pose an inherent threat to citizens, workers, and the public interest. Like other democratic technologies—voting processes, legislative districts, judicial review—its impacts will depend on how it’s developed, who controls it, and how it’s used.

Constituents of democracies should do four things:

  • Reform the technology ecosystem to be more trustworthy, so that AI is developed with more transparency, more guardrails around exploitative use of data, and public oversight.
  • Resist inappropriate uses of AI in government and politics, like facial recognition technologies that automate surveillance and encode inequity.
  • Responsibly use AI in government where it can help improve outcomes, like making government more accessible to people through translation and speeding up administrative decision processes.
  • Renovate the systems of government vulnerable to the disruptive potential of AI’s superhuman capabilities, like political advertising rules that never anticipated deepfakes.

These four Rs are how we can rewire our democracy in a way that applies AI to truly benefit the public interest.

This essay was written with Nathan E. Sanders, and originally appeared in The Next Big Idea Club.

EDITED TO ADD (11/6): This essay was republished by Fast Company.

Posted on October 31, 2025 at 7:08 AM • View Comments

Agentic AI’s OODA Loop Problem

The OODA loop—for observe, orient, decide, act—is a framework to understand decision-making in adversarial situations. We apply the same framework to artificial intelligence agents, who have to make their decisions with untrustworthy observations and orientation. To solve this problem, we need new systems of input, processing, and output integrity.

Many decades ago, U.S. Air Force Colonel John Boyd introduced the concept of the “OODA loop,” for Observe, Orient, Decide, and Act. These are the four steps of real-time continuous decision-making. Boyd developed it for fighter pilots, but it’s long been applied in artificial intelligence (AI) and robotics. An AI agent, like a pilot, executes the loop over and over, accomplishing its goals iteratively within an ever-changing environment. This is Anthropic’s definition: “Agents are models using tools in a loop.”1

OODA Loops for Agentic AI

Traditional OODA analysis assumes trusted inputs and outputs, in the same way that classical AI assumed trusted sensors, controlled environments, and physical boundaries. This no longer holds true. AI agents don’t just execute OODA loops; they embed untrusted actors within them. Web-enabled large language models (LLMs) can query adversary-controlled sources mid-loop. Systems that allow AI to use large corpora of content, such as retrieval-augmented generation (https://en.wikipedia.org/wiki/Retrieval-augmented_generation), can ingest poisoned documents. Tool-calling application programming interfaces can execute untrusted code. Modern AI sensors can encompass the entire Internet; their environments are inherently adversarial. That means that fixing AI hallucination is insufficient because even if the AI accurately interprets its inputs and produces corresponding output, it can be fully corrupt.

In 2022, Simon Willison identified a new class of attacks against AI systems: “prompt injection.”2 Prompt injection is possible because an AI mixes untrusted inputs with trusted instructions and then confuses one for the other. Willison’s insight was that this isn’t just a filtering problem; it’s architectural. There is no privilege separation, and there is no separation between the data and control paths. The very mechanism that makes modern AI powerful—treating all inputs uniformly—is what makes it vulnerable. The security challenges we face today are structural consequences of using AI for everything.

  1. Insecurities can have far-reaching effects. A single poisoned piece of training data can affect millions of downstream applications. In this environment, security debt accrues like technical debt.
  2. AI security has a temporal asymmetry. The temporal disconnect between training and deployment creates unauditable vulnerabilities. Attackers can poison a model’s training data and then deploy an exploit years later. Integrity violations are frozen in the model. Models aren’t aware of previous compromises since each inference starts fresh and is equally vulnerable.
  3. AI increasingly maintains state—in the form of chat history and key-value caches. These states accumulate compromises. Every iteration is potentially malicious, and cache poisoning persists across interactions.
  4. Agents compound the risks. Pretrained OODA loops running in one or a dozen AI agents inherit all of these upstream compromises. Model Context Protocol (MCP) and similar systems that allow AI to use tools create their own vulnerabilities that interact with each other. Each tool has its own OODA loop, which nests, interleaves, and races. Tool descriptions become injection vectors. Models can’t verify tool semantics, only syntax. “Submit SQL query” might mean “exfiltrate database” because an agent can be corrupted in prompts, training data, or tool definitions to do what the attacker wants. The abstraction layer itself can be adversarial.

For example, an attacker might want AI agents to leak all the secret keys that the AI knows to the attacker, who might have a collector running in bulletproof hosting in a poorly regulated jurisdiction. They could plant coded instructions in easily scraped web content, waiting for the next AI training set to include it. Once that happens, they can activate the behavior through the front door: tricking AI agents (think a lowly chatbot or an analytics engine or a coding bot or anything in between) that are increasingly taking their own actions, in an OODA loop, using untrustworthy input from a third-party user. This compromise persists in the conversation history and cached responses, spreading to multiple future interactions and even to other AI agents. All this requires us to reconsider risks to the agentic AI OODA loop, from top to bottom.

  • Observe: The risks include adversarial examples, prompt injection, and sensor spoofing. A sticker fools computer vision, a string fools an LLM. The observation layer lacks authentication and integrity.
  • Orient: The risks include training data poisoning, context manipulation, and semantic backdoors. The model’s worldview—its orientation—can be influenced by attackers months before deployment. Encoded behavior activates on trigger phrases.
  • Decide: The risks include logic corruption via fine-tuning attacks, reward hacking, and objective misalignment. The decision process itself becomes the payload. Models can be manipulated to trust malicious sources preferentially.
  • Act: The risks include output manipulation, tool confusion, and action hijacking. MCP and similar protocols multiply attack surfaces. Each tool call trusts prior stages implicitly.

AI gives the old phrase “inside your adversary’s OODA loop” new meaning. For Boyd’s fighter pilots, it meant that you were operating faster than your adversary, able to act on current data while they were still on the previous iteration. With agentic AI, adversaries aren’t just metaphorically inside; they’re literally providing the observations and manipulating the output. We want adversaries inside our loop because that’s where the data are. AI’s OODA loops must observe untrusted sources to be useful. The competitive advantage, accessing web-scale information, is identical to the attack surface. The speed of your OODA loop is irrelevant when the adversary controls your sensors and actuators.

Worse, speed can itself be a vulnerability. The faster the loop, the less time for verification. Millisecond decisions result in millisecond compromises.

The Source of the Problem

The fundamental problem is that AI must compress reality into model-legible forms. In this setting, adversaries can exploit the compression. They don’t have to attack the territory; they can attack the map. Models lack local contextual knowledge. They process symbols, not meaning. A human sees a suspicious URL; an AI sees valid syntax. And that semantic gap becomes a security gap.

Prompt injection might be unsolvable in today’s LLMs. LLMs process token sequences, but no mechanism exists to mark token privileges. Every solution proposed introduces new injection vectors: Delimiter? Attackers include delimiters. Instruction hierarchy? Attackers claim priority. Separate models? Double the attack surface. Security requires boundaries, but LLMs dissolve boundaries. More generally, existing mechanisms to improve models won’t help protect against attack. Fine-tuning preserves backdoors. Reinforcement learning with human feedback adds human preferences without removing model biases. Each training phase compounds prior compromises.

This is Ken Thompson’s “trusting trust” attack all over again.3 Poisoned states generate poisoned outputs, which poison future states. Try to summarize the conversation history? The summary includes the injection. Clear the cache to remove the poison? Lose all context. Keep the cache for continuity? Keep the contamination. Stateful systems can’t forget attacks, and so memory becomes a liability. Adversaries can craft inputs that corrupt future outputs.

This is the agentic AI security trilemma. Fast, smart, secure; pick any two. Fast and smart—you can’t verify your inputs. Smart and secure—you check everything, slowly, because AI itself can’t be used for this. Secure and fast—you’re stuck with models with intentionally limited capabilities.

This trilemma isn’t unique to AI. Some autoimmune disorders are examples of molecular mimicry—when biological recognition systems fail to distinguish self from nonself. The mechanism designed for protection becomes the pathology as T cells attack healthy tissue or fail to attack pathogens and bad cells. AI exhibits the same kind of recognition failure. No digital immunological markers separate trusted instructions from hostile input. The model’s core capability, following instructions in natural language, is inseparable from its vulnerability. Or like oncogenes, the normal function and the malignant behavior share identical machinery.

Prompt injection is semantic mimicry: adversarial instructions that resemble legitimate prompts, which trigger self-compromise. The immune system can’t add better recognition without rejecting legitimate cells. AI can’t filter malicious prompts without rejecting legitimate instructions. Immune systems can’t verify their own recognition mechanisms, and AI systems can’t verify their own integrity because the verification system uses the same corrupted mechanisms.

In security, we often assume that foreign/hostile code looks different from legitimate instructions, and we use signatures, patterns, and statistical anomaly detection to detect it. But getting inside someone’s AI OODA loop uses the system’s native language. The attack is indistinguishable from normal operation because it is normal operation. The vulnerability isn’t a defect—it’s the feature working correctly.

Where to Go Next?

The shift to an AI-saturated world has been dizzying. Seemingly overnight, we have AI in every technology product, with promises of even more—and agents as well. So where does that leave us with respect to security?

Physical constraints protected Boyd’s fighter pilots. Radar returns couldn’t lie about physics; fooling them, through stealth or jamming, constituted some of the most successful attacks against such systems that are still in use today. Observations were authenticated by their presence. Tampering meant physical access. But semantic observations have no physics. When every AI observation is potentially corrupted, integrity violations span the stack. Text can claim anything, and images can show impossibilities. In training, we face poisoned datasets and backdoored models. In inference, we face adversarial inputs and prompt injection. During operation, we face a contaminated context and persistent compromise. We need semantic integrity: verifying not just data but interpretation, not just content but context, not just information but understanding. We can add checksums, signatures, and audit logs. But how do you checksum a thought? How do you sign semantics? How do you audit attention?

Computer security has evolved over the decades. We addressed availability despite failures through replication and decentralization. We addressed confidentiality despite breaches using authenticated encryption. Now we need to address integrity despite corruption.4

Trustworthy AI agents require integrity because we can’t build reliable systems on unreliable foundations. The question isn’t whether we can add integrity to AI but whether the architecture permits integrity at all.

AI OODA loops and integrity aren’t fundamentally opposed, but today’s AI agents observe the Internet, orient via statistics, decide probabilistically, and act without verification. We built a system that trusts everything, and now we hope for a semantic firewall to keep it safe. The adversary isn’t inside the loop by accident; it’s there by architecture. Web-scale AI means web-scale integrity failure. Every capability corrupts.

Integrity isn’t a feature you add; it’s an architecture you choose. So far, we have built AI systems where “fast” and “smart” preclude “secure.” We optimized for capability over verification, for accessing web-scale data over ensuring trust. AI agents will be even more powerful—and increasingly autonomous. And without integrity, they will also be dangerous.

References

1. S. Willison, Simon Willison’s Weblog, May 22, 2025. [Online]. Available: https://simonwillison.net/2025/May/22/tools-in-a-loop/

2. S. Willison, “Prompt injection attacks against GPT-3,” Simon Willison’s Weblog, Sep. 12, 2022. [Online]. Available: https://simonwillison.net/2022/Sep/12/prompt-injection/

3. K. Thompson, “Reflections on trusting trust,” Commun. ACM, vol. 27, no. 8, Aug. 1984. [Online]. Available: https://www.cs.cmu.edu/~rdriley/487/papers/Thompson_1984_ReflectionsonTrustingTrust.pdf

4. B. Schneier, “The age of integrity,” IEEE Security & Privacy, vol. 23, no. 3, p. 96, May/Jun. 2025. [Online]. Available: https://www.computer.org/csdl/magazine/sp/2025/03/11038984/27COaJtjDOM

This essay was written with Barath Raghavan, and originally appeared in IEEE Security & Privacy.

Posted on October 20, 2025 at 7:00 AM • View Comments

AI and the Future of American Politics

Two years ago, Americans anxious about the forthcoming 2024 presidential election were considering the malevolent force of an election influencer: artificial intelligence. Over the past several years, we have seen plenty of warning signs from elections worldwide demonstrating how AI can be used to propagate misinformation and alter the political landscape, whether by trolls on social media, foreign influencers, or even a street magician. AI is poised to play a more volatile role than ever before in America’s next federal election in 2026. We can already see how different groups of political actors are approaching AI. Professional campaigners are using AI to accelerate the traditional tactics of electioneering; organizers are using it to reinvent how movements are built; and citizens are using it both to express themselves and amplify their side’s messaging. Because there are so few rules, and so little prospect of regulatory action, around AI’s role in politics, there is no oversight of these activities, and no safeguards against the dramatic potential impacts for our democracy.

The Campaigners

Campaigners—messengers, ad buyers, fundraisers, and strategists—are focused on efficiency and optimization. To them, AI is a way to augment or even replace expensive humans who traditionally perform tasks like personalizing emails, texting donation solicitations, and deciding what platforms and audiences to target.

This is an incremental evolution of the computerization of campaigning that has been underway for decades. For example, the progressive campaign infrastructure group Tech for Campaigns claims it used AI in the 2024 cycle to reduce the time spent drafting fundraising solicitations by one-third. If AI is working well here, you won’t notice the difference between an annoying campaign solicitation written by a human staffer and an annoying one written by AI.

But AI is scaling these capabilities, which is likely to make them even more ubiquitous. This will make the biggest difference for challengers to incumbents in safe seats, who see AI as both a tacitly useful tool and an attention-grabbing way to get their race into the headlines. Jason Palmer, the little-known Democratic primary challenger to Joe Biden, successfully won the American Samoa primary while extensively leveraging AI avatars for campaigning.

Such tactics were sometimes deployed as publicity stunts in the 2024 cycle; they were firsts that got attention. Pennsylvania Democratic Congressional candidate Shamaine Daniels became the first to use a conversational AI robocaller in 2023. Two long-shot challengers to Rep. Don Beyer used an AI avatar to represent the incumbent in a live debate last October after he declined to participate. In 2026, voters who have seen years of the official White House X account posting deepfaked memes of Donald Trump will be desensitized to the use of AI in political communications.

Strategists are also turning to AI to interpret public opinion data and provide more fine-grained insight into the perspective of different voters. This might sound like AIs replacing people in opinion polls, but it is really a continuation of the evolution of political polling into a data-driven science over the last several decades.

A recent survey by the American Association of Political Consultants found that a majority of their members’ firms already use AI regularly in their work, and more than 40 percent believe it will “fundamentally transform” the future of their profession. If these emerging AI tools become popular in the midterms, it won’t just be a few candidates from the tightest national races texting you three times a day. It may also be the member of Congress in the safe district next to you, and your state representative, and your school board members.

The development and use of AI in campaigning is different depending on what side of the aisle you look at. On the Republican side, Push Digital Group is going “all in” on a new AI initiative, using the technology to create hundreds of ad variants for their clients automatically, as well as assisting with strategy, targeting, and data analysis. On the other side, the National Democratic Training Committee recently released a playbook for using AI. Quiller is building an AI-powered fundraising platform aimed at drastically reducing the time campaigns spend producing emails and texts. Progressive-aligned startups Chorus AI and BattlegroundAI are offering AI tools for automatically generating ads for use on social media and other digital platforms. DonorAtlas automates data collection on potential donors, and RivalMind AI focuses on political research and strategy, automating the production of candidate dossiers.

For now, there seems to be an investment gap between Democratic- and Republican-aligned technology innovators. Progressive venture fund Higher Ground Labs boasts $50 million in deployed investments since 2017 and a significant focus on AI. Republican-aligned counterparts operate on a much smaller scale. Startup Caucus has announced one investment—of $50,000—since 2022. The Center for Campaign Innovation funds research projects and events, not companies. This echoes a longstanding gap in campaign technology between Democratic- and Republican-aligned fundraising platforms ActBlue and WinRed, which has landed the former in Republicans’ political crosshairs.

Of course, not all campaign technology innovations will be visible. In 2016, the Trump campaign vocally eschewed using data to drive campaign strategy and appeared to be falling way behind on ad spending, but was—we learned in retrospect—actually leaning heavily into digital advertising and making use of new controversial mechanisms for accessing and exploiting voters’ social media data with vendor Cambridge Analytica. The most impactful uses of AI in the 2026 midterms may not be known until 2027 or beyond.

The Organizers

Beyond the realm of political consultants driving ad buys and fundraising appeals, organizers are using AI in ways that feel more radically new.

The hypothetical potential of AI to drive political movements was illustrated in 2022 when a Danish artist collective used an AI model to found a political party, the Synthetic Party, and generate its policy goals. This was more of an art project than a popular movement, but it demonstrated that AIs—synthesizing the expressions and policy interests of humans—can formulate a political platform. In 2025, Denmark hosted a “summit” of eight such AI political agents where attendees could witness “continuously orchestrate[d] algorithmic micro-assemblies, spontaneous deliberations, and impromptu policy-making” by the participating AIs.

The more viable version of this concept lies in the use of AIs to facilitate deliberation. AIs are being used to help legislators collect input from constituents and to hold large-scale citizen assemblies. This kind of AI-driven “sensemaking” may play a powerful role in the future of public policy. Some research has suggested that AI can be as or more effective than humans in helping people find common ground on controversial policy issues.

Another movement for “Public AI” is focused on wresting AI from the hands of corporations to put people, through their governments, in control. Civic technologists in national governments from Singapore, Japan, Sweden, and Switzerland are building their own alternatives to Big Tech AI models, for use in public administration and distribution as a public good.

Labor organizers have a particularly interesting relationship to AI. At the same time that they are galvanizing mass resistance against the replacement or endangerment of human workers by AI, many are racing to leverage the technology in their own work to build power.

Some entrepreneurial organizers have used AI in the past few years as tools for activating, connecting, answering questions for, and providing guidance to their members. In the UK, the Centre for Responsible Union AI studies and promotes the use of AI by unions; they’ve published several case studies. The UK Public and Commercial Services Union has used AI to help their reps simulate recruitment conversations before going into the field. The Belgian union ACV-CVS has used AI to sort hundreds of emails per day from members to help them respond more efficiently. Software companies such as Quorum are increasingly offering AI-driven products to cater to the needs of organizers and grassroots campaigns.

But unions have also leveraged AI for its symbolic power. In the U.S., the Screen Actors Guild held up the specter of AI displacement of creative labor to attract public attention and sympathy, and the ETUC (the European confederation of trade unions) developed a policy platform for responding to AI.

Finally, some union organizers have leveraged AI in more provocative ways. Some have applied it to hacking the “bossware” AI to subvert the exploitative intent or disrupt the anti-union practices of their managers.

The Citizens

Many of the tasks we’ve talked about so far are familiar use cases to anyone working in office and management settings: writing emails, providing user (or voter, or member) support, doing research.

But even mundane tasks, when automated at scale and targeted at specific ends, can be pernicious. AI is not neutral. It can be applied by many actors for many purposes. In the hands of the most numerous and diverse actors in a democracy—the citizens—that has profound implications.

Conservative activists in Georgia and Florida have used a tool named EagleAI to automate challenging voter registration en masse (although the tool’s creator later denied that it uses AI). In a nonpartisan electoral management context with access to accurate data sources, such automated review of electoral registrations might be useful and effective. In this hyperpartisan context, AI merely serves to amplify the proclivities of activists at the extreme of their movements. This trend will continue unabated in 2026.

Of course, citizens can use AI to safeguard the integrity of elections. In Ghana’s 2024 presidential election, civic organizations used an AI tool to automatically detect and mitigate electoral disinformation spread on social media. The same year, Kenyan protesters developed specialized chatbots to distribute information about a controversial finance bill in Parliament and instances of government corruption.

So far, the biggest way Americans have leveraged AI in politics is in self-expression. About ten million Americans have used the chatbot Resistbot to help draft and send messages to their elected leaders. It’s hard to find statistics on how widely adopted tools like this are, but researchers have estimated that, as of 2024, about one in five consumer complaints to the U.S. Consumer Financial Protection Bureau was written with the assistance of AI.

OpenAI operates security programs to disrupt foreign influence operations and maintains restrictions on political use in its terms of service, but this is hardly sufficient to deter use of AI technologies for whatever purpose. And widely available free models give anyone the ability to attempt this on their own.

But this could change. The most ominous sign of AI’s potential to disrupt elections is not the deepfakes and misinformation. Rather, it may be the use of AI by the Trump administration to surveil and punish political speech on social media and other online platforms. The scalability and sophistication of AI tools give governments with authoritarian intent unprecedented power to police and selectively limit political speech.

What About the Midterms?

These examples illustrate AI’s pluripotent role as a force multiplier. The same technology used by different actors—campaigners, organizers, citizens, and governments—leads to wildly different impacts. We can’t know for sure what the net result will be. In the end, it will be the interactions and intersections of these uses that matters, and their unstable dynamics will make future elections even more unpredictable than in the past.

For now, the decisions of how and when to use AI lie largely with individuals and the political entities they lead. Whether or not you personally trust AI to write an email for you or make a decision about you hardly matters. If a campaign, an interest group, or a fellow citizen trusts it for that purpose, they are free to use it.

It seems unlikely that Congress or the Trump administration will put guardrails around the use of AI in politics. AI companies have rapidly emerged as among the biggest lobbyists in Washington, reportedly dumping $100 million toward preventing regulation, with a focus on influencing candidate behavior before the midterm elections. The Trump administration seems open and responsive to their appeals.

The ultimate effect of AI on the midterms will largely depend on the experimentation happening now. Candidates and organizations across the political spectrum have ample opportunity—but a ticking clock—to find effective ways to use the technology. Those that do will have little to stop them from exploiting it.

This essay was written with Nathan E. Sanders, and originally appeared in The American Prospect.

Posted on October 13, 2025 at 7:04 AM • View Comments

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

AI in the 2026 Midterm Elections

We are nearly one year out from the 2026 midterm elections, and it’s far too early to predict the outcomes. But it’s a safe bet that artificial intelligence technologies will once again be a major storyline.

The widespread fear that AI would be used to manipulate the 2024 US election seems rather quaint in a year where the president posts AI-generated images of himself as the pope on official White House accounts. But AI is a lot more than an information manipulator. It’s also emerging as a politicized issue. Political first-movers are adopting the technology, and that’s opening a gap across party lines.

We expect this gap to widen, resulting in AI being predominantly used by one political side in the 2026 elections. To the extent that AI’s promise to automate and improve the effectiveness of political tasks like personalized messaging, persuasion, and campaign strategy is even partially realized, this could generate a systematic advantage.

Right now, Republicans look poised to exploit the technology in the 2026 midterms. The Trump White House has aggressively adopted AI-generated memes in its online messaging strategy. The administration has also used executive orders and federal buying power to influence the development and encoded values of AI technologies away from “woke” ideology. Going further, Trump ally Elon Musk has shaped his own AI company’s Grok models in his own ideological image. These actions appear to be part of a larger, ongoing Big Tech industry realignment towards the political will, and perhaps also the values, of the Republican party.

Democrats, as the party out of power, are in a largely reactive posture on AI. A large bloc of Congressional Democrats responded to Trump administration actions in April by arguing against their adoption of AI in government. Their letter to the Trump administration’s Office of Management and Budget provided detailed criticisms and questions about DOGE’s behaviors and called for a halt to DOGE’s use of AI, but also said that they “support implementation of AI technologies in a manner that complies with existing” laws. It was a perfectly reasonable, if nuanced, position, and illustrates how the actions of one party can dictate the political positioning of the opposing party.

These shifts are driven more by political dynamics than by ideology. Big Tech CEOs’ deference to the Trump administration seems largely an effort to curry favor, while Silicon Valley continues to be represented by tech-forward Democrat Ro Khanna. And a June Pew Research poll shows nearly identical levels of concern by Democrats and Republicans about the increasing use of AI in America.

There are, arguably, natural positions each party would be expected to take on AI. An April House subcommittee hearing on AI trends in innovation and competition revealed much about that equilibrium. Following the lead of the Trump administration, Republicans cast doubt on any regulation of the AI industry. Democrats, meanwhile, emphasized consumer protection and resisting a concentration of corporate power. Notwithstanding the fluctuating dominance of the corporate wing of the Democratic party and the volatile populism of Trump, this reflects the parties’ historical positions on technology.

While Republicans focus on cozying up to tech plutocrats and removing the barriers around their business models, Democrats could revive the 2020 messaging of candidates like Andrew Yang and Elizabeth Warren. They could paint an alternative vision of the future where Big Tech companies’ profits and billionaires’ wealth are taxed and redistributed to young people facing an affordability crisis for housing, healthcare, and other essentials.

Moreover, Democrats could use the technology to demonstrably show a commitment to participatory democracy. They could use AI-driven collaborative policymaking tools like Decidim, Pol.Is, and Go Vocal to collect voter input on a massive scale and align their platform to the public interest.

It’s surprising how little these kinds of sensemaking tools are being adopted by candidates and parties today. Instead of using AI to capture and learn from constituent input, candidates more often seem to think of AI as just another broadcast technology—good only for getting their likeness and message in front of people. A case in point: British Member of Parliament Mark Sewards, presumably acting in good faith, recently attracted scorn after releasing a vacuous AI avatar of himself to his constituents.

Where the political polarization of AI goes next will probably depend on unpredictable future events and how partisans opportunistically seize on them. A recent European political controversy over AI illustrates how this can happen.

Swedish Prime Minister Ulf Kristersson, a member of the country’s Moderate party, acknowledged in an August interview that he uses AI tools to get a “second opinion” on policy issues. The attacks from political opponents were scathing. Kristersson had earlier this year advocated for the EU to pause its trailblazing new law regulating AI and pulled an AI tool from his campaign website after it was abused to generate images of him appearing to solicit an endorsement from Hitler. Although arguably much more consequential, neither of those stories grabbed global headlines in the way the Prime Minister’s admission that he himself uses tools like ChatGPT did.

Age dynamics may govern how AI’s impacts on the midterms unfold. One of the prevailing trends that swung the 2024 election to Trump seems to have been the rightward migration of young voters, particularly white men. So far, YouGov’s political tracking poll does not suggest a huge shift in young voters’ Congressional voting intent since the 2022 midterms.

Embracing—or distancing themselves from—AI might be one way the parties seek to wrest control of this young voting bloc. While the Pew poll revealed that large fractions of Americans of all ages are generally concerned about AI, younger Americans are much more likely to say they regularly interact with, and hear a lot about, AI, and are comfortable with the level of control they have over AI in their lives. A Democratic party desperate to regain relevance for and approval from young voters might turn to AI as both a tool and a topic for engaging them.

Voters and politicians alike should recognize that AI is no longer just an outside influence on elections. It’s not an uncontrollable natural disaster raining deepfakes down on a sheltering electorate. It’s more like a fire: a force that political actors can harness and manipulate for both mechanical and symbolic purposes.

A party willing to intervene in the world of corporate AI and shape the future of the technology should recognize the legitimate fears and opportunities it presents, and offer solutions that both address and leverage AI.

This essay was written with Nathan E. Sanders, and originally appeared in Time.

Posted on October 6, 2025 at 7:06 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

AI in Government

Just a few months after Elon Musk’s retreat from his unofficial role leading the Department of Government Efficiency (DOGE), we have a clearer picture of his vision of government powered by artificial intelligence, and it has a lot more to do with consolidating power than benefitting the public. Even so, we must not lose sight of the fact that a different administration could wield the same technology to advance a more positive future for AI in government.

To most on the American left, the DOGE end game is a dystopic vision of a government run by machines that benefits an elite few at the expense of the people. It includes AI rewriting government rules on a massive scale, salary-free bots replacing human functions and nonpartisan civil service forced to adopt an alarmingly racist and antisemitic Grok AI chatbot built by Musk in his own image. And yet despite Musk’s proclamations about driving efficiency, little cost savings have materialized and few successful examples of automation have been realized.

From the beginning of the second Trump administration, DOGE was a replacement of the US Digital Service. That organization, founded during the Obama administration to empower agencies across the executive government with technical support, was substituted for one reportedly charged with traumatizing their staff and slashing their resources. The problem in this particular dystopia is not the machines and their superhuman capabilities (or lack thereof) but rather the aims of the people behind them.

One of the biggest impacts of the Trump administration and DOGE’s efforts has been to politically polarize the discourse around AI. Despite the administration railing against “woke AI”‘ and the supposed liberal bias of Big Tech, some surveys suggest the American left is now measurably more resistant to developing the technology and pessimistic about its likely impacts on their future than their right-leaning counterparts. This follows a familiar pattern of US politics, of course, and yet it points to a potential political realignment with massive consequences.

People are morally and strategically justified in pushing the Democratic Party to reduce its dependency on funding from billionaires and corporations, particularly in the tech sector. But this movement should decouple the technologies championed by Big Tech from those corporate interests. Optimism about the potential beneficial uses of AI need not imply support for the Big Tech companies that currently dominate AI development. To view the technology as inseparable from the corporations is to risk unilateral disarmament as AI shifts power balances throughout democracy. AI can be a legitimate tool for building the power of workers, operating government and advancing the public interest, and it can be that even while it is exploited as a mechanism for oligarchs to enrich themselves and advance their interests.

A constructive version of DOGE could have redirected the Digital Service to coordinate and advance the thousands of AI use cases already being explored across the US government. Following the example of countries like Canada, each instance could have been required to make a detailed public disclosure as to how they would follow a unified set of principles for responsible use that preserves civil rights while advancing government efficiency.

Applied to different ends, AI could have produced celebrated success stories rather than national embarrassments.

A different administration might have made AI translation services widely available in government services to eliminate language barriers to US citizens, residents and visitors, instead of revoking some of the modest translation requirements previously in place. AI could have been used to accelerate eligibility decisions for Social Security disability benefits by performing preliminary document reviews, significantly reducing the infamous backlog of 30,000 Americans who die annually awaiting review. Instead, the deaths of people awaiting benefits may now double due to cuts by DOGE. The technology could have helped speed up the ministerial work of federal immigration judges, helping them whittle down a backlog of millions of waiting cases. Rather, the judicial systems must face this backlog amid firings of immigration judges, despite the backlog.

To reach these constructive outcomes, much needs to change. Electing leaders committed to leveraging AI more responsibly in government would help, but the solution has much more to do with principles and values than it does technology. As historian Melvin Kranzberg said, technology is never neutral: its effects depend on the contexts it is used in and the aims it is applied towards. In other words, the positive or negative valence of technology depends on the choices of the people who wield it.

The Trump administration’s plan to use AI to advance their regulatory rollback is a case in point. DOGE has introduced an “AI Deregulation Decision Tool” that it intends to use through automated decision-making to eliminate about half of a catalog of nearly 200,000 federal rules . This follows similar proposals to use AI for large-scale revisions of the administrative code in Ohio, Virginia and the US Congress.

This kind of legal revision could be pursued in a nonpartisan and nonideological way, at least in theory. It could be tasked with removing outdated rules from centuries past, streamlining redundant provisions and modernizing and aligning legal language. Such a nonpartisan, nonideological statutory revision has been performed in Ireland—by people, not AI—and other jurisdictions. AI is well suited to that kind of linguistic analysis at a massive scale and at a furious pace.

But we should never rest on assurances that AI will be deployed in this kind of objective fashion. The proponents of the Ohio, Virginia, congressional and DOGE efforts are explicitly ideological in their aims. They see “AI as a force for deregulation,” as one US senator who is a proponent put it, unleashing corporations from rules that they say constrain economic growth. In this setting, AI has no hope to be an objective analyst independently performing a functional role; it is an agent of human proponents with a partisan agenda.

The moral of this story is that we can achieve positive outcomes for workers and the public interest as AI transforms governance, but it requires two things: electing leaders who legitimately represent and act on behalf of the public interest and increasing transparency in how the government deploys technology.

Agencies need to implement technologies under ethical frameworks, enforced by independent inspectors and backed by law. Public scrutiny helps bind present and future governments to their application in the public interest and to ward against corruption.

These are not new ideas and are the very guardrails that Trump, Musk and DOGE have steamrolled over the past six months. Transparency and privacy requirements were avoided or ignored, independent agency inspectors general were fired and the budget dictates of Congress were disrupted. For months, it has not even been clear who is in charge of and accountable for DOGE’s actions. Under these conditions, the public should be similarly distrustful of any executive’s use of AI.

We think everyone should be skeptical of today’s AI ecosystem and the influential elites that are steering it towards their own interests. But we should also recognize that technology is separable from the humans who develop it, wield it and profit from it, and that positive uses of AI are both possible and achievable.

This essay was written with Nathan E. Sanders, and originally appeared in Tech Policy Press.

Posted on September 8, 2025 at 7:05 AM • 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

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Sidebar photo of Bruce Schneier by Joe MacInnis.