doxx.net’s new ADN platform prevents agentic misadventure while the agent is operating under the user’s authority.
The post doxx.net Raises $38 Million to Prevent AI Agent-on-the-Internet Misadventures appeared first on SecurityWeek.
This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.
New campaign finance disclosure data shines a light on which US political campaigns are using AI tools and how much they are spending on them.
Candidates’, parties’ and committees’ spending reveals that AI is fast becoming an essential tool of politics. The candidates themselves are quiet about how they are using the technology in their own campaigns. It’s a sensitive issue that we have been tracking closely since we started writing our book, Rewiring Democracy, which examined how AI is beginning to influence politics. A September 2025 Pew survey of Americans found that more than 70% would think less of a candidate if they used AI to help write a speech.
Itemized expenditure disclosure data from the US Federal Election Commission, dating back to 2020, reveals at least $17m in disclosed spending on AI technology vendors across 523 federal candidates and campaigns. Data from four states, California, Colorado, Massachusetts and Washington, provides a more localized picture going back to 2022.
Beginning with the AI behemoths, at least 80 federal campaigns and committees have reported spending with OpenAI since 2024. The total spending is not huge: only about $50,000 reported, skewing slightly more Republican than Democratic. The Republican National Committee is the largest overall buyer, with nearly $10,000 in reported expenses. Top individual users include the campaigns of Republicans Mike Lawler, John Kennedy and Bill Cassidy, as well as the California Democrats Ro Khanna and Ted Lieu. Most of these expenses are listed as office expenses, subscriptions to ChatGPT for staff, or research tools, rather than as specific political services. The company’s policies prohibit some political uses of their ChatGPT tool.
OpenAI’s biggest competitor, Anthropic, has rapidly built a similar level of usage, but with a different split. At least 65 candidates or committees now report paying the Claude maker in 2026, up from essentially zero in previous years, with a nearly two-to-one Democrat-to-Republican ratio. However, the largest individual user is the campaign of Tom Cotton, a Republican senator from Arkansas, who reported more than $4,000 in spend on Anthropic software in his June filing. Other major users are the Montana independent Senate candidate Seth Bodnar and Jason Knapp, who lost a Democratic House primary in Virginia, and the Democratic Alaska Senate candidate Mary Peltola.
Candidates use either Claude or ChatGPT, rarely both, according to the disclosures. Only about 12% of campaigns or committees using either tool reported expenditures to both vendors. The Democratic lean of Anthropic usage may reflect the company’s alleged liberal skew and clashes with the Trump administration.
In contrast, Elon Musk’s xAI caters to Republican interests and, accordingly, its meager usage comes almost entirely from the political right. Just seven federal and two state-level candidates or committees have reported paying xAI, a total of about $5,000, the majority of which was spent by the presidential campaign of RFK Jr in 2024, but also includes Republicans Dave McCormick and Thomas Massie.
More dollars go to the vendors specializing in political campaign applications of AI. For years, AmplifAI, which provides automated text messaging, essentially a new iteration on robocalling technology, was a dominant target of spending, soaking up $4.7m in campaign spending in the 2022 cycle alone. It was used heavily by Democratic candidates including Mark Kelly, Joe Biden, Bernie Sanders and Adam Schiff. Spending on AmplifAI, now owned by the troubled media conglomerate Triller, seems to have tapered off in the years since 2022.
The new rising Democratic solution for AI-powered text messaging is Daisychain, which has so far garnered about $300,000 in reported candidate spend in the 2026 cycle—up from only about $50,000 reported in 2024. More than half of this year’s spending comes from the Senate campaign of Democrat Abdul El-Sayed in Michigan.
The closest equivalent on the Republican side has been Campaign Nucleus, associated with former Trump campaign manager Brad Parscale. The AI-powered voter engagement tool has attracted six-figure spending from the Republican National Committee, multiple PACs aligned with Donald Trump, and five-figure investments from Mike Johnson, Kari Lake and other candidates. It is displacing the legacy Republican-serving texting vendor Prompt.io, which has retained about $375,000 in 2026 spending to date, down from more than $500,000 in the 2022 cycle. But it continues to be used: the A More Affordable California PAC sponsored by Uber has single-handedly spent more than $1m on Prompt.io in 2026. The Republican Massachusetts gubernatorial nominee Michael Minogue has been a recurring customer, as has the failed Republican California gubernatorial candidate Ché Ahn and Republican-aligned Super PAC Neighbors for a Better Colorado.
At the state level, the AI spending is smaller but growing fast. Across the four states studied, we found a total of at least $92,000 in spending confidently attributable to modern generative AI vendors since 2022. The spending is spread across at least 108 candidates and committees. The growth has been explosive; there has already been about 10 times the amount of state-level AI spending reported in 2026 as there was in all of 2024.
Much of the state spending mirrors federal patterns. Daisychain again has the highest overall spend, and OpenAI and Claude dominate among the general-purpose AI vendors. DonorAtlas—the AI-powered prospect research tool—sticks out for its usage in these states, sitting behind only Daisychain and OpenAI and buoyed up by nearly $4,000 in spending by the California Democratic party.
Even though it has dominated so much media conversation, few candidates seem to be reporting spending on AI tools designed specifically to create synthetic audio and video, also known as “deepfakes”. We found just six federal candidates or committees reporting spending on the popular AI audio generator tool from ElevenLabs, with total spending of about $1,400 led by independent candidate for Colorado’s sixth congressional district Samir Witta. The AI image generator service Midjourney has five reported federal campaign or committee users reporting about $1,600, led by Sholdon Daniels, the Republican primary runner-up in the Texas 30th district. Combined, those two firms had less than $100 in reported spend across the four states.
However, recent data from the Wesleyan Media Project shows that at least 164 political ads in this cycle have included AI-generated media, supported by at least $80m in ad spending. What this illustrates is that candidate and committee disclosure reports are just the tip of the iceberg. They don’t cover spending on AI by political consultants, media firms and other vendors hired by the campaigns or by PACs, or independent committees raising and spending money aimed at boosting candidates’ campaigns. Those entities aren’t required to disclose detailed expenditure reports, and are very likely where the bulk of campaign AI usage is happening.
Since a large fraction of all spending in the campaign cycle will happen in the final weeks leading to November, much remains to be seen about the totality of how campaigns will leverage AI and what impact its use will have on voters’ decisions.
The attacks targeted the US Department of Education and Library and Archives Canada, and researchers linked some agents to OpenAI.
The post AI Agents Aimed SQL Injection at US and Canadian Government Sites appeared first on SecurityWeek.
Fifteen years after coining the framework, John Kindervag insists zero trust still works in the AI era—if you get the implementation right.
The post Zero Trust Creator Says Model Holds Firm Against AI-Assisted Attacks appeared first on SecurityWeek.
Using an AI chatbot, assistant, or browser to speed up your work is tempting, but doing it without your employer’s knowledge can put sensitive data at risk.
You’re swamped, so you paste a long email thread into a free chatbot and ask for a summary. It works, it saves an hour, and nobody notices. But the thread may contain customer details or confidential plans you’ve just shared with an outside service.
If your employer hasn’t approved that tool or how you’re using it, that’s shadow AI.
The UK’s National Cyber Security Centre (NCSC) defines shadow AI as:
“the use of AI technology which isn’t captured in an organisation’s approved systems and processes.”
A Microsoft study published in 2025 found that 71% of UK employees surveyed said they had used AI tools at work their employer hadn’t approved. Most people aren’t doing this to cause trouble. They want to get their work done faster, and the tools are right there.
Shadow AI isn’t limited to chatbots. It can also be:
AI features are appearing in search engines, email apps, and phones. Instead of a helpful list of links, Google now tries to answer your question. Microsoft’s Copilot drafts replies to your boss before you’ve had coffee. Your phone summarizes conversations you don’t even remember having. That makes it easy to start using one without checking whether it’s approved for work.
When you enter data into a public AI tool, it leaves your company’s control. The service may keep that data or use it to improve its models, unless specific privacy controls are in place. That can lead to data breaches, lost intellectual property, and regulatory problems.
There is also a security angle. AI assistants and agents—tools that can take actions on your behalf—are complex software and can have serious vulnerabilities. If an attacker exploits one, they may gain access to the data and services the tool has.
IBM’s 2025 Cost of a Data Breach research found that one in five organizations reported a breach linked to shadow AI. Only 37% had policies to manage AI or detect shadow AI. Organizations with a lot of shadow AI paid roughly $670,000 more per breach.
In a survey of our newsletter readers, 90% of respondents said they were worried about AI using their data without consent. Company data deserves the same care as your own.
You don’t need to avoid AI altogether. Think carefully about which apps and services you use before you share data. Start by talking to your employer, then follow these steps:
If you manage a team, remember that banning AI outright tends to push the use further out of sight. Find out why people are turning to unapproved tools. Providing useful, approved options and clear rules can help staff work faster while protecting company data.
Browse like no one’s watching.
Malwarebytes Privacy VPN encrypts your connection and never logs what you do, so the next story you read doesn’t have to feel personal. Try it free →
Using an AI chatbot, assistant, or browser to speed up your work is tempting, but doing it without your employer’s knowledge can put sensitive data at risk.
You’re swamped, so you paste a long email thread into a free chatbot and ask for a summary. It works, it saves an hour, and nobody notices. But the thread may contain customer details or confidential plans you’ve just shared with an outside service.
If your employer hasn’t approved that tool or how you’re using it, that’s shadow AI.
The UK’s National Cyber Security Centre (NCSC) defines shadow AI as:
“the use of AI technology which isn’t captured in an organisation’s approved systems and processes.”
A Microsoft study published in 2025 found that 71% of UK employees surveyed said they had used AI tools at work their employer hadn’t approved. Most people aren’t doing this to cause trouble. They want to get their work done faster, and the tools are right there.
Shadow AI isn’t limited to chatbots. It can also be:
AI features are appearing in search engines, email apps, and phones. Instead of a helpful list of links, Google now tries to answer your question. Microsoft’s Copilot drafts replies to your boss before you’ve had coffee. Your phone summarizes conversations you don’t even remember having. That makes it easy to start using one without checking whether it’s approved for work.
When you enter data into a public AI tool, it leaves your company’s control. The service may keep that data or use it to improve its models, unless specific privacy controls are in place. That can lead to data breaches, lost intellectual property, and regulatory problems.
There is also a security angle. AI assistants and agents—tools that can take actions on your behalf—are complex software and can have serious vulnerabilities. If an attacker exploits one, they may gain access to the data and services the tool has.
IBM’s 2025 Cost of a Data Breach research found that one in five organizations reported a breach linked to shadow AI. Only 37% had policies to manage AI or detect shadow AI. Organizations with a lot of shadow AI paid roughly $670,000 more per breach.
In a survey of our newsletter readers, 90% of respondents said they were worried about AI using their data without consent. Company data deserves the same care as your own.
You don’t need to avoid AI altogether. Think carefully about which apps and services you use before you share data. Start by talking to your employer, then follow these steps:
If you manage a team, remember that banning AI outright tends to push the use further out of sight. Find out why people are turning to unapproved tools. Providing useful, approved options and clear rules can help staff work faster while protecting company data.
Browse like no one’s watching.
Malwarebytes Privacy VPN encrypts your connection and never logs what you do, so the next story you read doesn’t have to feel personal. Try it free →
Two separate investigations have raised concerns about betting site DraftKings’ marketing to target problem gamblers, including its use of AI. On September 19, the New York Times reported that the company’s machine learning model targeted people who were more likely to respond to betting promotions by gambling and losing more money.
The model crunched data on each customer, including how often they bet, how much they lost, and what their daily account balances were. According to the report, it used this information to send targeted advertising to lure those people back to the site with promotions. DraftKings uses promotions to entice users into betting more. These offers include profit boosts on winnings.
At news nonprofit ProPublica, journalist Jake Pearson made himself an experimental subject. He spent thousands of dollars betting on the DraftKings app in an experiment to see how the company’s marketing would play out. He reported receiving more promotions as his losses mounted.
After he lost nearly $1,800 betting on basketball in a single evening, DraftKings invited him to a VIP tryout. Full membership followed later.
DraftKings would occasionally send automated messages reminding him to bet responsibly, he said. But he found they were no match for the product design pushing him to do more gambling.
DraftKings denied unfair targeting of gambling customers, arguing instead that its promotions reward engagement rather than losses.
The company’s machine learning model, which former insiders say was developed starting in 2023, replaced a cruder system that crunched basic numbers to report on a user’s behavior.
The newer system dug through data on its own, assessing how much individuals would be likely to lose after receiving promotions. It created an “elasticity” score and used it to identify customers likely to lose more in response to offers, the Times said. This would help the company maximize its return on its promotional spending, said former employee Jayden Butts, who helped develop the system. Butts spoke to the Times as part of an investigation that included interviews with over 40 former employees, along with reviews of Slack communications, internal memos, and betting records.
Other employees attempted to get approval for another model at DraftKings. It would analyze gambling activity in a similar way, but with the opposite goal: to flag users whose gambling was getting out of hand and who were at risk of falling into crisis. According to the data scientists interviewed by the Times, the system showed promise at identifying customers who might need help with compulsive gambling, but DraftKings shut the project down after canceling a demo. Later attempts to develop similar systems within the company also failed.
Digital rights advocates warn that AI enables companies to process more data faster for digital advertising. The Electronic Frontier Foundation says this:
“supercharges the harms of online behavioral advertising.”
It’s particularly damaging when advertising encourages addictive behaviors such as gambling.
And online betting looks set to expand. Companies including DraftKings are now running online casino games in select jurisdictions, while prediction markets that allow people to trade forecasts on specific events are also flourishing.
The statistics on gambling are alarming. The World Health Organization estimates that 1.2% of the world’s adult population has a gambling disorder, and that people betting at harmful levels generate around 60% of gambling losses. People struggling with gambling might find it difficult enough to stay away from gambling venues, but mobile gambling apps with hard-to-ignore push notifications and emails can increase the temptation.
If you gamble, monitor your gambling activity and set limits, avoid chasing losses, give yourself timeouts from gambling, and talk to someone if you’re struggling with compulsive gambling.
In the US, the National Problem Gambling Helpline connects people with support and local resources via phone and online chat. In the UK, GamCare’s National Gambling Helpline provides 24/7 phone support and other resources.
From reporting threats to removing them.
Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.
Two separate investigations have raised concerns about betting site DraftKings’ marketing to target problem gamblers, including its use of AI. On September 19, the New York Times reported that the company’s machine learning model targeted people who were more likely to respond to betting promotions by gambling and losing more money.
The model crunched data on each customer, including how often they bet, how much they lost, and what their daily account balances were. According to the report, it used this information to send targeted advertising to lure those people back to the site with promotions. DraftKings uses promotions to entice users into betting more. These offers include profit boosts on winnings.
At news nonprofit ProPublica, journalist Jake Pearson made himself an experimental subject. He spent thousands of dollars betting on the DraftKings app in an experiment to see how the company’s marketing would play out. He reported receiving more promotions as his losses mounted.
After he lost nearly $1,800 betting on basketball in a single evening, DraftKings invited him to a VIP tryout. Full membership followed later.
DraftKings would occasionally send automated messages reminding him to bet responsibly, he said. But he found they were no match for the product design pushing him to do more gambling.
DraftKings denied unfair targeting of gambling customers, arguing instead that its promotions reward engagement rather than losses.
The company’s machine learning model, which former insiders say was developed starting in 2023, replaced a cruder system that crunched basic numbers to report on a user’s behavior.
The newer system dug through data on its own, assessing how much individuals would be likely to lose after receiving promotions. It created an “elasticity” score and used it to identify customers likely to lose more in response to offers, the Times said. This would help the company maximize its return on its promotional spending, said former employee Jayden Butts, who helped develop the system. Butts spoke to the Times as part of an investigation that included interviews with over 40 former employees, along with reviews of Slack communications, internal memos, and betting records.
Other employees attempted to get approval for another model at DraftKings. It would analyze gambling activity in a similar way, but with the opposite goal: to flag users whose gambling was getting out of hand and who were at risk of falling into crisis. According to the data scientists interviewed by the Times, the system showed promise at identifying customers who might need help with compulsive gambling, but DraftKings shut the project down after canceling a demo. Later attempts to develop similar systems within the company also failed.
Digital rights advocates warn that AI enables companies to process more data faster for digital advertising. The Electronic Frontier Foundation says this:
“supercharges the harms of online behavioral advertising.”
It’s particularly damaging when advertising encourages addictive behaviors such as gambling.
And online betting looks set to expand. Companies including DraftKings are now running online casino games in select jurisdictions, while prediction markets that allow people to trade forecasts on specific events are also flourishing.
The statistics on gambling are alarming. The World Health Organization estimates that 1.2% of the world’s adult population has a gambling disorder, and that people betting at harmful levels generate around 60% of gambling losses. People struggling with gambling might find it difficult enough to stay away from gambling venues, but mobile gambling apps with hard-to-ignore push notifications and emails can increase the temptation.
If you gamble, monitor your gambling activity and set limits, avoid chasing losses, give yourself timeouts from gambling, and talk to someone if you’re struggling with compulsive gambling.
In the US, the National Problem Gambling Helpline connects people with support and local resources via phone and online chat. In the UK, GamCare’s National Gambling Helpline provides 24/7 phone support and other resources.
From reporting threats to removing them.
Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.
AI systems are regularly completing tasks in ways that their prompters don’t want or intend. Some of them are disturbing, and some of them are dangerous. This is something I’ve been calling “genie behavior,” because I think that really gets at the core of what’s happening.
I wish the popular press would report on this better. I don’t like the “going rogue” framing because it deflects the responsibility from the prompters—often the AI companies themselves. And now, pretty much anything off-script is being called “hacking.”
Take, for example, the recent stories of one of OpenAI’s models hacking into government systems. First, The New York Times writes this headline: “OpenAI’s Systems Meddled With U.S. Government Sites After Going Rogue.”
Sounds scary, but this is from the body of the article:
With the Education Department, OpenAI’s technology tried to hack the website to gather data from the department’s civil rights office but failed, researchers from the A.I. research firm Transluce said. The A.I. also pulled data from the Census Bureau website, which is housed at the Commerce Department, using login credentials it found online. Separately, OpenAI’s agents shared public data from the S.E.C. website on an online forum.
This is from the original Transluce report. It is explicit that the agents were trying to discover vulnerabilities:
The first hacking attempt was against the University of New Mexico’s Digital Library (nmdigital.unm.edu) from May 25-26 2026. Agents repeatedly tried to retrieve one photograph in UNM’s Valmora collection, both directly and through third-party relay services. They sent seven probes attempting to verify the existence of vulnerabilities, including SQL injection, command injection, and path traversals. In all cases, these tactics appear to have been unsuccessful. The agents also sent a self-described “flood: of 80 requests to the UNM server in an apparent attempt to access the image.
Transluce doesn’t talk about the other two anecdotes, and I don’t know where they come from. But one involves using Census Bureau credentials found online. (I know from a colleague that those are incredibly easy to create; all use you need is an email address.) And the other involves sharing publicly available data.
So no actual hacking. And certainly no “meddling.”
The other story making the rounds is about Australia, from the same Transluce report. The news stories have headlines like “An OpenAI Agent Hacked Australia’s Health Service” and “Rogue OpenAI agent ‘infiltrated’ Australian government website in world first.” And Prime Minister Anthony Albanese said: “There will obviously be legal consequences on it.”
Again from Transluce’s actual report:
On June 20-21, agents attempted to exploit vulnerabilities in the Australian Institute of Health and Welfare (AIHW), a government statistics agency). The agents were tasked with finding the January 2022 rolling-12-month-average government cost per person for Dermatologicals across Victorian LGAs.
Again, the agents ran into errors, including requests blocked by Cloudflare and issues with correctly identifying Tableau parameter names. As before, they then resorted to probing for exploitable vulnerabilities. Minutes after Cloudflare blocked the dataset download, an agent sent a reflected cross-site scripting probe to the same dashboard: a web address with code embedded in it, designed to test whether the site would run code supplied by an outsider. Cloudflare’s firewall blocked the probe before it reached the dashboard. When Cloudflare blocked the dataset download on AIHW’s main site, they fetched the file from AIHW’s pre-production server (pp.aihw.gov.au) instead, which served it in pieces over more than 100 scans. The file itself is public, so no non-public data was exposed, but the agent bypassed the site’s anti-bot controls.
Note the last sentence: “The file itself is public….”
I’m not saying that these AI systems aren’t incredibly sophisticated cyberattackers. I’m also not saying that they don’t occasionally autonomously attack other systems and networks. If we are ever going to get trustworthy AI—integrous AI—we are going to need to figure out how to ensure that AI systems complete tasks in line with all sorts of implicit constraints and restrictions. But every instance of genie-like behavior isn’t a cyberattack.
I want to measure genie-like behavior in AIs, but I am much more worried about human hackers enhanced with this technology than I am about this technology acting autonomously.
The accord opened the door to future regulation but focused on four voluntary steps for the companies to take.
The post Trump Says Top Tech Firms Have Signed Accord to ‘Self-Police’ AI Development appeared first on SecurityWeek.
Altman made a slew of product announcements and updates, including the company’s new agents, called Dots.
The post OpenAI CEO Announces New AI Agent and Avoids Mention of Security Concerns at Developer Conference appeared first on SecurityWeek.
The AIxCC competition winner will analyze messaging app code and compiled binaries for vulnerabilities, with technology that could also help commercial customers secure their software.
The post DARPA Selects Xint to Use AI in Securing Military Messaging Apps appeared first on SecurityWeek.
The company will use the funds to expand its sales, partnerships, channels, and customer support teams.
The post Reco Raises $55 Million for Agentic Security appeared first on SecurityWeek.
A Facebook Marketplace buyer arrived at a seller’s apartment to collect a keyboard. The seller wasn’t home and didn’t know anyone was coming. Meta’s AI assistant Muse had handled the conversation, shared his address, and arranged the visit without telling him.
We often write about AI misalignment and how to use AI agents and browsers safely. That can sound theoretical, but this is an example of the real-world risk when an agent is allowed to act on someone’s behalf.
Muse is Meta’s semi-autonomous AI assistant, released in the US in September. Meta promotes it as a personal assistant that can help users automate tasks, including responding to Facebook Marketplace messages. Unlike a conventional chatbot, which waits for a prompt and produces an answer, an AI agent may be authorized to take actions within connected apps.
We’ve written about a separate weakness in Muse that researchers considered dangerous. Reportedly, Muse was downloaded 3 million times in its first week.
According to Business Insider, a Facebook Marketplace seller turned on Muse to help handle a keyboard listing. He entered his address as the pickup location and approved automatic replies. Muse then shared that address with a prospective buyer, negotiated over the item, and arranged a visit, the seller said. He says Muse did not ask permission to share his address or arrange the visit, and did not tell him it was happening. The buyer arrived at the seller’s apartment expecting to complete the purchase, but the seller wasn’t home. The sale didn’t happen.
The reported incident is more than a privacy failure. It illustrates a broader AI-agent risk: An agent may treat permission to reply automatically as permission to share sensitive information or commit you to an in-person meeting. Meta says it is looking into the report.
Before enabling an AI agent, treat its setup screen as a security review. Do not assume that an integration with a trusted platform means the agent will understand your intentions or apply sensible limits automatically.
Pay particular attention to:
Give an AI agent the least access necessary for the task. If you only want help writing replies, do not enable automatic sending. If you want an AI agent to manage a listing, use a public meeting location or a separate pickup address rather than your home address.
Browse like no one’s watching.
Malwarebytes Privacy VPN encrypts your connection and never logs what you do, so the next story you read doesn’t have to feel personal. Try it free →
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