The EU is recommending import controls to combat unregulated squid fishing in the Southwest Atlantic. I’m not optimistic.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
St. Lucie County in Florida discovered (alt link) a dozen Flock cameras whose ownership it can’t identify, and that the county government had not permitted.
I am reminded of the decade-old story of StingRay cell phone surveillance devices in Washington, DC, whose operators were also unknown.
My guess is that in the StingRay case, the devices were operated by foreign actors. This Flock case is more likely some local government entity that didn’t bother getting approval. Were I a foreign actor, I would rather hack the existing Flock network—like Israel did with Tehran’s surveillance cameras—than risk installing my own.
Regardless, once we normalize a surveillance infrastructure, both friends and foes will take advantage of it.
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.
Researchers at Northeastern University, in collaboration with Consumer Reports, evaluated how much modern cars spy in their drivers:
The new Northeastern study shows, for the first time, data flowing among the vehicles, the vehicle apps you download when you buy your car, and third-party companies, documenting exactly what kind of data gets siphoned from our vehicles and which companies are receiving that information.
Basically, your car’s manufacturer has you under constant surveillance, and they use that data against you.
The companies on the receiving end of your data, our investigation has found, include car insurers and lenders that are partners in “telematics data exchanges,” which compile driving data on millions of drivers, thousands of data brokers that create personalized risk scores, companies selling infotainment and WiFi hotspot products, and even local and state government agencies working on planning, traffic, and safety initiatives.
Nearly every automaker sent data to outside companies.
Even more troubling, almost a quarter of the vehicle apps were found to be sending out personally identifiable information, including vehicle owners’ names, vehicle identification numbers (VINs), and precise geographic locations. That information can make it easy for companies to link driving behavior to personal data profiles created by data brokers and marketers. Such profiles are routinely sold to banks, insurers, pharmaceutical companies, lenders, and retailers, who can use it for personalized loan terms and filtered bank and insurance offers, a CR and CalMatters investigation found.
Remember the adage “If you’re not the customer, then you’re the product”? (The sentiment is older than you think.) Turns out that with modern internet-connected everything, you’re the product even if you are the customer.
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.
Modern messaging apps allow users to link their phone accounts to their computer desktop. Eavesdroppers are taking advantage of this capability:
Apps such as WhatsApp Web and Signal Desktop allow people to use their accounts on other devices, such as laptops or desktop computers.
Germany’s Customs Office has been using these features to connect a police-controlled computer to a suspect’s account.
Once connected, messages can be delivered to that computer without the police having to crack the encryption protecting them.
Netzpoltik details that police are able to gain access in this way either through physical access to someone’s phone or by intercepting verification codes via a state-sanctioned phishing attack or intercepting SMS messages via telephone surveillance.
That last paragraph is important. Making this work requires user consent.
What we want is a feature that displays connected devices, so users could notice if a new device gets connected to their account.
ArsTechnica is reporting on a “new” attack against RSA, one that bypasses factoring.
First, this attack isn’t new. The original research is from 2007. What is new is the implementation.
Second, it is a forgery attack. It allows an attacker to forge digital signatures. It does not recover the private key from the public key.
Third, the attack only works against pure signatures. That is, signatures without any formatting or padding. This is not generally how we use RSA in practice.
Fourth, speed is all relative. This is not a polynomial-time algorithm; it’s a subexponential-time algorithm. But it is somewhat faster than factoring. The authors were able to forge messages for 1024-bit RSA with 1380 CPU core-years (over five real-world months).
The authors have a webpage that explains the context much better than the article. And here’s the paper.
EDITED TO ADD: Slashdot thread.
I feel like someone who reads this blog will want to go to this:
Families are invited to dive into the fascinating world of marine biology during an exciting, hands-on Family Squid Dissection at the Hands-On Science Center. Designed for curious learners of all ages, this unique experience combines an interactive lesson with the opportunity to explore the anatomy and adaptations of real ocean life.
[…]
During the guided squid dissection, each family will work together to examine a squid up close, exploring its organs, structures, and specialized features. The experience provides a memorable opportunity for children and adults to see firsthand how the anatomy of a squid helps it survive in its underwater environment.
If you go, take pictures.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
Earlier this month, Anthropic published a long report detailing all of the Claude misuses it detected. Daniel Meissler usefully summarized the report into 117 findings.
A few of the highlights:
- AI agents increasingly handled reconnaissance, exploitation, data theft, propaganda production, surveillance workflows, and research while humans selected targets, set goals, and reviewed important outputs.
- The report describes attackers using AI to industrialize credential theft, cloud compromise, phishing, vulnerability research, and the extraction of sensitive data from downstream organizations.
- Influence operations used persistent agent memory, fake news sites, fabricated journalists, synthetic personas, political profiling, and large-scale multilingual content, although high content volume often produced little genuine engagement.
- Surveillance and repression cases included automated dossiers, biometric and communications analysis, transnational targeting, coercive recruitment, and systems that continued operating locally after model access was revoked.
- Biological and weapons cases show dual-use risk: AI supported advanced scientific and military work, but the report generally doesn’t establish completed biological weapons or operational battlefield deployment.
This is an impressive piece of malware. Its sophistication says nation-state to me, but there is no direct evidence and certainly no attribution.
Sidebar photo of Bruce Schneier by Joe MacInnis.