Schneier on Security

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AI Summarization Optimization

These days, the most important meeting attendee isn’t a person: It’s the AI notetaker.

This system assigns action items and determines the importance of what is said. If it becomes necessary to revisit the facts of the meeting, its summary is treated as impartial evidence.

But clever meeting attendees can manipulate this system’s record by speaking more to what the underlying AI weights for summarization and importance than to their colleagues. As a result, you can expect some meeting attendees to use language more likely to be captured in summaries, timing their interventions strategically, repeating key points, and employing formulaic phrasing that AI models are more likely to pick up on. Welcome to the world of AI summarization optimization (AISO).

Optimizing for algorithmic manipulation

AI summarization optimization has a well-known precursor: SEO.

Search-engine optimization is as old as the World Wide Web. The idea is straightforward: Search engines scour the internet digesting every possible page, with the goal of serving the best results to every possible query. The objective for a content creator, company, or cause is to optimize for the algorithm search engines have developed to determine their webpage rankings for those queries. That requires writing for two audiences at once: human readers and the search-engine crawlers indexing content. Techniques to do this effectively are passed around like trade secrets, and a $75 billion industry offers SEO services to organizations of all sizes.

More recently, researchers have documented techniques for influencing AI responses, including large-language model optimization (LLMO) and generative engine optimization (GEO). Tricks include content optimization—adding citations and statistics—and adversarial approaches: using specially crafted text sequences. These techniques often target sources that LLMs heavily reference, such as Reddit, which is claimed to be cited in 40% of AI-generated responses. The effectiveness and real-world applicability of these methods remains limited and largely experimental, although there is substantial evidence that countries such as Russia are actively pursuing this.

AI summarization optimization follows the same logic on a smaller scale. Human participants in a meeting may want a certain fact highlighted in the record, or their perspective to be reflected as the authoritative one. Rather than persuading colleagues directly, they adapt their speech for the notetaker that will later define the “official” summary. For example:

“The main factor in last quarter’s delay was supply chain disruption.”
“The key outcome was overwhelmingly positive client feedback.”
“Our takeaway here is in alignment moving forward.”
“What matters here is the efficiency gains, not the temporary cost overrun.”

The techniques are subtle. They employ high-signal phrases such as “key takeaway” and “action item,” keep statements short and clear, and repeat them when possible. They also use contrastive framing (“this, not that”), and speak early in the meeting or at transition points.

Once spoken words are transcribed, they enter the model’s input. Cue phrases—and even transcription errors—can steer what makes it into the summary. In many tools, the output format itself is also a signal: Summarizers often offer sections such as “Key Takeaways” or “Action Items,” so language that mirrors those headings is more likely to be included. In effect, well-chosen phrases function as implicit markers that guide the AI toward inclusion.

Research confirms this. Early AI summarization research showed that models trained to reconstruct summary-style sentences systematically overweigh such content. Models over-rely on early-position content in news. And models often overweigh statements at the start or end of a transcript, underweighting the middle. Recent work further confirms vulnerability to phrasing-based manipulation: models cannot reliably distinguish embedded instructions from ordinary content, especially when phrasing mimics salient cues.

How to combat AISO

If AISO becomes common, three forms of defense will emerge. First, meeting participants will exert social pressure on one another. When researchers secretly deployed AI bots in Reddit’s r/changemyview community, users and moderators responded with strong backlash calling it “psychological manipulation.” Anyone using obvious AI-gaming phrases may face similar disapproval.

Second, organizations will start governing meeting behavior using AI: risk assessments and access restrictions before the meetings even start, detection of AISO techniques in meetings, and validation and auditing after the meetings.

Third, AI summarizers will have their own technical countermeasures. For example, the AI security company CloudSEK recommends content sanitization to strip suspicious inputs, prompt filtering to detect meta-instructions and excessive repetition, context window balancing to weight repeated content less heavily, and user warnings showing content provenance.

Broader defenses could draw from security and AI safety research: preprocessing content to detect dangerous patterns, consensus approaches requiring consistency thresholds, self-reflection techniques to detect manipulative content, and human oversight protocols for critical decisions. Meeting-specific systems could implement additional defenses: tagging inputs by provenance, weighting content by speaker role or centrality with sentence-level importance scoring, and discounting high-signal phrases while favoring consensus over fervor.

Reshaping human behavior

AI summarization optimization is a small, subtle shift, but it illustrates how the adoption of AI is reshaping human behavior in unexpected ways. The potential implications are quietly profound.

Meetings—humanity’s most fundamental collaborative ritual—are being silently reengineered by those who understand the algorithm’s preferences. The articulate are gaining an invisible advantage over the wise. Adversarial thinking is becoming routine, embedded in the most ordinary workplace rituals, and, as AI becomes embedded in organizational life, strategic interactions with AI notetakers and summarizers may soon be a necessary executive skill for navigating corporate culture.

AI summarization optimization illustrates how quickly humans adapt communication strategies to new technologies. As AI becomes more embedded in workplace communication, recognizing these emerging patterns may prove increasingly important.

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

Tags: AI, hacking, LLM

Posted on November 3, 2025 at 7:05 AM • View Comments

Friday Squid Blogging: Giant Squid at the Smithsonian

I can’t believe that I haven’t yet posted this picture of a giant squid at the Smithsonian.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Tags: squid

Posted on October 31, 2025 at 5:06 PM • View Comments

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.

Tags: AI, democracy, LLM

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

The AI-Designed Bioweapon Arms Race

Interesting article about the arms race between AI systems that invent/design new biological pathogens, and AI systems that detect them before they’re created:

The team started with a basic test: use AI tools to design variants of the toxin ricin, then test them against the software that is used to screen DNA orders. The results of the test suggested there was a risk of dangerous protein variants slipping past existing screening software, so the situation was treated like the equivalent of a zero-day vulnerability.

[…]

Details of that original test are being made available today as part of a much larger analysis that extends the approach to a large range of toxic proteins. Starting with 72 toxins, the researchers used three open source AI packages to generate a total of about 75,000 potential protein variants.

And this is where things get a little complicated. Many of the AI-designed protein variants are going to end up being non-functional, either subtly or catastrophically failing to fold up into the correct configuration to create an active toxin.

[…]

In any case, DNA sequences encoding all 75,000 designs were fed into the software that screens DNA orders for potential threats. One thing that was very clear is that there were huge variations in the ability of the four screening programs to flag these variant designs as threatening. Two of them seemed to do a pretty good job, one was mixed, and another let most of them through. Three of the software packages were updated in response to this performance, which significantly improved their ability to pick out variants.

There was also a clear trend in all four screening packages: The closer the variant was to the original structurally, the more likely the package (both before and after the patches) was to be able to flag it as a threat. In all cases, there was also a cluster of variant designs that were unlikely to fold into a similar structure, and these generally weren’t flagged as threats.

The research is all preliminary, and there are a lot of ways in which the experiment diverges from reality. But I am not optimistic about this particular arms race. I think that the ability of AI systems to create something deadly will advance faster than the ability of AI systems to detect its components.

Tags: AI, biological warfare

Posted on October 30, 2025 at 7:05 AM • View Comments

Signal’s Post-Quantum Cryptographic Implementation

Signal has just rolled out its quantum-safe cryptographic implementation.

Ars Technica has a really good article with details:

Ultimately, the architects settled on a creative solution. Rather than bolt KEM onto the existing double ratchet, they allowed it to remain more or less the same as it had been. Then they used the new quantum-safe ratchet to implement a parallel secure messaging system.

Now, when the protocol encrypts a message, it sources encryption keys from both the classic Double Ratchet and the new ratchet. It then mixes the two keys together (using a cryptographic key derivation function) to get a new encryption key that has all of the security of the classical Double Ratchet but now has quantum security, too.

The Signal engineers have given this third ratchet the formal name: Sparse Post Quantum Ratchet, or SPQR for short. The third ratchet was designed in collaboration with PQShield, AIST, and New York University. The developers presented the erasure-code-based chunking and the high-level Triple Ratchet design at the Eurocrypt 2025 conference. At the Usenix 25 conference, they discussed the six options they considered for adding quantum-safe forward secrecy and post-compromise security and why SPQR and one other stood out. Presentations at the NIST PQC Standardization Conference and the Cryptographic Applications Workshop explain the details of chunking, the design challenges, and how the protocol had to be adapted to use the standardized ML-KEM.

Jacomme further observed:

The final thing interesting for the triple ratchet is that it nicely combines the best of both worlds. Between two users, you have a classical DH-based ratchet going on one side, and fully independently, a KEM-based ratchet is going on. Then, whenever you need to encrypt something, you get a key from both, and mix it up to get the actual encryption key. So, even if one ratchet is fully broken, be it because there is now a quantum computer, or because somebody manages to break either elliptic curves or ML-KEM, or because the implementation of one is flawed, or…, the Signal message will still be protected by the second ratchet. In a sense, this update can be seen, of course simplifying, as doubling the security of the ratchet part of Signal, and is a cool thing even for people that don’t care about quantum computers.

Louvre Jewel Heist

I assume I don’t have to explain last week’s Louvre jewel heist. I love a good caper, and have (like many others) eagerly followed the details. An electric ladder to a second-floor window, an angle grinder to get into the room and the display cases, security guards there more to protect patrons than valuables—seven minutes, in and out.

There were security lapses:

The Louvre, it turns out—at least certain nooks of the ancient former palace—is something like an anopticon: a place where no one is observed. The world now knows what the four thieves (two burglars and two accomplices) realized as recently as last week: The museum’s Apollo Gallery, which housed the stolen items, was monitored by a single outdoor camera angled away from its only exterior point of entry, a balcony. In other words, a free-roaming Roomba could have provided the world’s most famous museum with more information about the interior of this space. There is no surveillance footage of the break-in.

Professional jewelry thieves were not impressed with the four. Here’s Larry Lawton:

“I robbed 25, 30 jewelry stores—20 million, 18 million, something like that,” Mr. Lawton said. “Did you know that I never dropped a ring or an earring, no less, a crown worth 20 million?”

He thinks that they had a co-conspirator on the inside.

Museums, especially smaller ones, are good targets for theft because they rarely secure what they hold to its true value. They can’t; it would be prohibitively expensive. This makes them an attractive target.

We might find out soon. It looks like some people have been arrested

Not being out of the country—out of the EU—by now was sloppy. Leaving DNA evidence was sloppy. I can hope the criminals were sloppy enough not to have disassembled the jewelry by now, but I doubt it. They were probably taken apart within hours of the theft.

The whole thing is sad, really. Unlike stolen paintings, those jewels have no value in their original form. They need to be taken apart and sold in pieces. But then their value drops considerably—so the end result is that most of the worth of those items disappears. It would have been much better to pay the thieves not to rob the Louvre.

Tags: France, operational security, theft

Posted on October 27, 2025 at 11:03 AM • View Comments

First Wap: A Surveillance Computer You’ve Never Heard Of

Mother Jones has a long article on surveillance arms manufacturers, their wares, and how they avoid export control laws:

Operating from their base in Jakarta, where permissive export laws have allowed their surveillance business to flourish, First Wap’s European founders and executives have quietly built a phone-tracking empire, with a footprint extending from the Vatican to the Middle East to Silicon Valley.

It calls its proprietary system Altamides, which it describes in promotional materials as “a unified platform to covertly locate the whereabouts of single or multiple suspects in real-time, to detect movement patterns, and to detect whether suspects are in close vicinity with each other.”

Altamides leaves no trace on the phones it targets, unlike spyware such as Pegasus. Nor does it require a target to click on a malicious link or show any of the telltale signs (such as overheating or a short battery life) of remote monitoring.

Its secret is shrewd use of the antiquated telecom language Signaling System No. 7, known as SS7, that phone carriers use to route calls and text messages. Any entity with SS7 access can send queries requesting information about which cell tower a phone subscriber is nearest to, an essential first step to sending a text message or making a call to that subscriber. But First Wap’s technology uses SS7 to zero in on phone numbers and trace the location of their users.

Much more in this Lighthouse Reports analysis.

Tags: cell phones, privacy, surveillance, tracking

Posted on October 27, 2025 at 7:08 AM • View Comments

Friday Squid Blogging: “El Pulpo The Squid”

There is a new cigar named “El Pulpo The Squid.” Yes, that means “The Octopus The Squid.”

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Tags: squid

Posted on October 24, 2025 at 5:07 PM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.