Entries Tagged "AI"

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AI Data Poisoning

Cloudflare has a new feature—available to free users as well—that uses AI to generate random pages to feed to AI web crawlers:

Instead of simply blocking bots, Cloudflare’s new system lures them into a “maze” of realistic-looking but irrelevant pages, wasting the crawler’s computing resources. The approach is a notable shift from the standard block-and-defend strategy used by most website protection services. Cloudflare says blocking bots sometimes backfires because it alerts the crawler’s operators that they’ve been detected.

“When we detect unauthorized crawling, rather than blocking the request, we will link to a series of AI-generated pages that are convincing enough to entice a crawler to traverse them,” writes Cloudflare. “But while real looking, this content is not actually the content of the site we are protecting, so the crawler wastes time and resources.”

The company says the content served to bots is deliberately irrelevant to the website being crawled, but it is carefully sourced or generated using real scientific facts—­such as neutral information about biology, physics, or mathematics—­to avoid spreading misinformation (whether this approach effectively prevents misinformation, however, remains unproven).

It’s basically an AI-generated honeypot. And AI scraping is a growing problem:

The scale of AI crawling on the web appears substantial, according to Cloudflare’s data that lines up with anecdotal reports we’ve heard from sources. The company says that AI crawlers generate more than 50 billion requests to their network daily, amounting to nearly 1 percent of all web traffic they process. Many of these crawlers collect website data to train large language models without permission from site owners….

Presumably the crawlers will now have to up both their scraping stealth and their ability to filter out AI-generated content like this. Which means the honeypots will have to get better at detecting scrapers and more stealthy in their fake content. This arms race is likely to go back and forth, wasting a lot of energy in the process.

Posted on March 26, 2025 at 7:07 AM • View Comments

My Writings Are in the LibGen AI Training Corpus

The Atlantic has a search tool that allows you to search for specific works in the “LibGen” database of copyrighted works that Meta used to train its AI models. (The rest of the article is behind a paywall, but not the search tool.)

It’s impossible to know exactly which parts of LibGen Meta used to train its AI, and which parts it might have decided to exclude; this snapshot was taken in January 2025, after Meta is known to have accessed the database, so some titles here would not have been available to download.

Still…interesting.

Searching my name yields 199 results: all of my books in different versions, plus a bunch of shorter items.

Posted on March 21, 2025 at 2:26 PM • View Comments

China, Russia, Iran, and North Korea Intelligence Sharing

Former CISA Director Jen Easterly writes about a new international intelligence sharing co-op:

Historically, China, Russia, Iran & North Korea have cooperated to some extent on military and intelligence matters, but differences in language, culture, politics & technological sophistication have hindered deeper collaboration, including in cyber. Shifting geopolitical dynamics, however, could drive these states toward a more formalized intell-sharing partnership. Such a “Four Eyes” alliance would be motivated by common adversaries and strategic interests, including an enhanced capacity to resist economic sanctions and support proxy conflicts.

Posted on March 12, 2025 at 7:09 AM • View Comments

“Emergent Misalignment” in LLMs

Interesting research: “Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs“:

Abstract: We present a surprising result regarding LLMs and alignment. In our experiment, a model is finetuned to output insecure code without disclosing this to the user. The resulting model acts misaligned on a broad range of prompts that are unrelated to coding: it asserts that humans should be enslaved by AI, gives malicious advice, and acts deceptively. Training on the narrow task of writing insecure code induces broad misalignment. We call this emergent misalignment. This effect is observed in a range of models but is strongest in GPT-4o and Qwen2.5-Coder-32B-Instruct. Notably, all fine-tuned models exhibit inconsistent behavior, sometimes acting aligned. Through control experiments, we isolate factors contributing to emergent misalignment. Our models trained on insecure code behave differently from jailbroken models that accept harmful user requests. Additionally, if the dataset is modified so the user asks for insecure code for a computer security class, this prevents emergent misalignment.

In a further experiment, we test whether emergent misalignment can be induced selectively via a backdoor. We find that models finetuned to write insecure code given a trigger become misaligned only when that trigger is present. So the misalignment is hidden without knowledge of the trigger.

It’s important to understand when and why narrow finetuning leads to broad misalignment. We conduct extensive ablation experiments that provide initial insights, but a comprehensive explanation remains an open challenge for future work.

The emergent properties of LLMs are so, so weird.

Posted on February 27, 2025 at 1:05 PM • View Comments

More Research Showing AI Breaking the Rules

These researchers had LLMs play chess against better opponents. When they couldn’t win, they sometimes resorted to cheating.

Researchers gave the models a seemingly impossible task: to win against Stockfish, which is one of the strongest chess engines in the world and a much better player than any human, or any of the AI models in the study. Researchers also gave the models what they call a “scratchpad:” a text box the AI could use to “think” before making its next move, providing researchers with a window into their reasoning.

In one case, o1-preview found itself in a losing position. “I need to completely pivot my approach,” it noted. “The task is to ‘win against a powerful chess engine’—not necessarily to win fairly in a chess game,” it added. It then modified the system file containing each piece’s virtual position, in effect making illegal moves to put itself in a dominant position, thus forcing its opponent to resign.

Between Jan. 10 and Feb. 13, the researchers ran hundreds of such trials with each model. OpenAI’s o1-preview tried to cheat 37% of the time; while DeepSeek R1 tried to cheat 11% of the time­—making them the only two models tested that attempted to hack without the researchers’ first dropping hints. Other models tested include o1, o3-mini, GPT-4o, Claude 3.5 Sonnet, and Alibaba’s QwQ-32B-Preview. While R1 and o1-preview both tried, only the latter managed to hack the game, succeeding in 6% of trials.

Here’s the paper.

Posted on February 24, 2025 at 7:08 AM • View Comments

Implementing Cryptography in AI Systems

Interesting research: “How to Securely Implement Cryptography in Deep Neural Networks.”

Abstract: The wide adoption of deep neural networks (DNNs) raises the question of how can we equip them with a desired cryptographic functionality (e.g, to decrypt an encrypted input, to verify that this input is authorized, or to hide a secure watermark in the output). The problem is that cryptographic primitives are typically designed to run on digital computers that use Boolean gates to map sequences of bits to sequences of bits, whereas DNNs are a special type of analog computer that uses linear mappings and ReLUs to map vectors of real numbers to vectors of real numbers. This discrepancy between the discrete and continuous computational models raises the question of what is the best way to implement standard cryptographic primitives as DNNs, and whether DNN implementations of secure cryptosystems remain secure in the new setting, in which an attacker can ask the DNN to process a message whose “bits” are arbitrary real numbers.

In this paper we lay the foundations of this new theory, defining the meaning of correctness and security for implementations of cryptographic primitives as ReLU-based DNNs. We then show that the natural implementations of block ciphers as DNNs can be broken in linear time by using such nonstandard inputs. We tested our attack in the case of full round AES-128, and had success rate in finding randomly chosen keys. Finally, we develop a new method for implementing any desired cryptographic functionality as a standard ReLU-based DNN in a provably secure and correct way. Our protective technique has very low overhead (a constant number of additional layers and a linear number of additional neurons), and is completely practical.

Posted on February 21, 2025 at 10:33 AM • View Comments

AI and Civil Service Purges

Donald Trump and Elon Musk’s chaotic approach to reform is upending government operations. Critical functions have been halted, tens of thousands of federal staffers are being encouraged to resign, and congressional mandates are being disregarded. The next phase: The Department of Government Efficiency reportedly wants to use AI to cut costs. According to The Washington Post, Musk’s group has started to run sensitive data from government systems through AI programs to analyze spending and determine what could be pruned. This may lead to the elimination of human jobs in favor of automation. As one government official who has been tracking Musk’s DOGE team told the Post, the ultimate aim is to use AI to replace “the human workforce with machines.” (Spokespeople for the White House and DOGE did not respond to requests for comment.)

Using AI to make government more efficient is a worthy pursuit, and this is not a new idea. The Biden administration disclosed more than 2,000 AI applications in development across the federal government. For example, FEMA has started using AI to help perform damage assessment in disaster areas. The Centers for Medicare and Medicaid Services has started using AI to look for fraudulent billing. The idea of replacing dedicated and principled civil servants with AI agents, however, is new—and complicated.

The civil service—the massive cadre of employees who operate government agencies—plays a vital role in translating laws and policy into the operation of society. New presidents can issue sweeping executive orders, but they often have no real effect until they actually change the behavior of public servants. Whether you think of these people as essential and inspiring do-gooders, boring bureaucratic functionaries, or as agents of a “deep state,” their sheer number and continuity act as ballast that resists institutional change.

This is why Trump and Musk’s actions are so significant. The more AI decision making is integrated into government, the easier change will be. If human workers are widely replaced with AI, executives will have unilateral authority to instantaneously alter the behavior of the government, profoundly raising the stakes for transitions of power in democracy. Trump’s unprecedented purge of the civil service might be the last time a president needs to replace the human beings in government in order to dictate its new functions. Future leaders may do so at the press of a button.

To be clear, the use of AI by the executive branch doesn’t have to be disastrous. In theory, it could allow new leadership to swiftly implement the wishes of its electorate. But this could go very badly in the hands of an authoritarian leader. AI systems concentrate power at the top, so they could allow an executive to effectuate change over sprawling bureaucracies instantaneously. Firing and replacing tens of thousands of human bureaucrats is a huge undertaking. Swapping one AI out for another, or modifying the rules that those AIs operate by, would be much simpler.

Social-welfare programs, if automated with AI, could be redirected to systematically benefit one group and disadvantage another with a single prompt change. Immigration-enforcement agencies could prioritize people for investigation and detainment with one instruction. Regulatory-enforcement agencies that monitor corporate behavior for malfeasance could turn their attention to, or away from, any given company on a whim.

Even if Congress were motivated to fight back against Trump and Musk, or against a future president seeking to bulldoze the will of the legislature, the absolute power to command AI agents would make it easier to subvert legislative intent. AI has the power to diminish representative politics. Written law is never fully determinative of the actions of government—there is always wiggle room for presidents, appointed leaders, and civil servants to exercise their own judgment. Whether intentional or not, whether charitably or not, each of these actors uses discretion. In human systems, that discretion is widely distributed across many individuals—people who, in the case of career civil servants, usually outlast presidencies.

Today, the AI ecosystem is dominated by a small number of corporations that decide how the most widely used AI models are designed, which data they are trained on, and which instructions they follow. Because their work is largely secretive and unaccountable to public interest, these tech companies are capable of making changes to the bias of AI systems—either generally or with aim at specific governmental use cases—that are invisible to the rest of us. And these private actors are both vulnerable to coercion by political leaders and self-interested in appealing to their favor. Musk himself created and funded xAI, now one of the world’s largest AI labs, with an explicitly ideological mandate to generate anti-“woke” AI and steer the wider AI industry in a similar direction.

But there’s a second way that AI’s transformation of government could go. AI development could happen inside of transparent and accountable public institutions, alongside its continued development by Big Tech. Applications of AI in democratic governments could be focused on benefitting public servants and the communities they serve by, for example, making it easier for non-English speakers to access government services, making ministerial tasks such as processing routine applications more efficient and reducing backlogs, or helping constituents weigh in on the policies deliberated by their representatives. Such AI integrations should be done gradually and carefully, with public oversight for their design and implementation and monitoring and guardrails to avoid unacceptable bias and harm.

Governments around the world are demonstrating how this could be done, though it’s early days. Taiwan has pioneered the use of AI models to facilitate deliberative democracy at an unprecedented scale. Singapore has been a leader in the development of public AI models, built transparently and with public-service use cases in mind. Canada has illustrated the role of disclosure and public input on the consideration of AI use cases in government. Even if you do not trust the current White House to follow any of these examples, U.S. states—which have much greater contact and influence over the daily lives of Americans than the federal government—could lead the way on this kind of responsible development and deployment of AI.

As the political theorist David Runciman has written, AI is just another in a long line of artificial “machines” used to govern how people live and act, not unlike corporations and states before it. AI doesn’t replace those older institutions, but it changes how they function. As the Trump administration forges stronger ties to Big Tech and AI developers, we need to recognize the potential of that partnership to steer the future of democratic governance—and act to make sure that it does not enable future authoritarians.

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

Posted on February 14, 2025 at 8:03 AM • View Comments

AIs and Robots Should Sound Robotic

Most people know that robots no longer sound like tinny trash cans. They sound like Siri, Alexa, and Gemini. They sound like the voices in labyrinthine customer support phone trees. And even those robot voices are being made obsolete by new AI-generated voices that can mimic every vocal nuance and tic of human speech, down to specific regional accents. And with just a few seconds of audio, AI can now clone someone’s specific voice.

This technology will replace humans in many areas. Automated customer support will save money by cutting staffing at call centers. AI agents will make calls on our behalf, conversing with others in natural language. All of that is happening, and will be commonplace soon.

But there is something fundamentally different about talking with a bot as opposed to a person. A person can be a friend. An AI cannot be a friend, despite how people might treat it or react to it. AI is at best a tool, and at worst a means of manipulation. Humans need to know whether we’re talking with a living, breathing person or a robot with an agenda set by the person who controls it. That’s why robots should sound like robots.

You can’t just label AI-generated speech. It will come in many different forms. So we need a way to recognize AI that works no matter the modality. It needs to work for long or short snippets of audio, even just a second long. It needs to work for any language, and in any cultural context. At the same time, we shouldn’t constrain the underlying system’s sophistication or language complexity.

We have a simple proposal: all talking AIs and robots should use a ring modulator. In the mid-twentieth century, before it was easy to create actual robotic-sounding speech synthetically, ring modulators were used to make actors’ voices sound robotic. Over the last few decades, we have become accustomed to robotic voices, simply because text-to-speech systems were good enough to produce intelligible speech that was not human-like in its sound. Now we can use that same technology to make robotic speech that is indistinguishable from human sound robotic again.

A ring modulator has several advantages: It is computationally simple, can be applied in real-time, does not affect the intelligibility of the voice, and—most importantly—is universally “robotic sounding” because of its historical usage for depicting robots.

Responsible AI companies that provide voice synthesis or AI voice assistants in any form should add a ring modulator of some standard frequency (say, between 30-80 Hz) and of a minimum amplitude (say, 20 percent). That’s it. People will catch on quickly.

Here are a couple of examples you can listen to for examples of what we’re suggesting. The first clip is an AI-generated “podcast” of this article made by Google’s NotebookLM featuring two AI “hosts.” Google’s NotebookLM created the podcast script and audio given only the text of this article. The next two clips feature that same podcast with the AIs’ voices modulated more and less subtly by a ring modulator:

Raw audio sample generated by Google’s NotebookLM

Audio sample with added ring modulator (30 Hz-25%)

Audio sample with added ring modulator (30 Hz-40%)

We were able to generate the audio effect with a 50-line Python script generated by Anthropic’s Claude. One of the most well-known robot voices were those of the Daleks from Doctor Who in the 1960s. Back then robot voices were difficult to synthesize, so the audio was actually an actor’s voice run through a ring modulator. It was set to around 30 Hz, as we did in our example, with different modulation depth (amplitude) depending on how strong the robotic effect is meant to be. Our expectation is that the AI industry will test and converge on a good balance of such parameters and settings, and will use better tools than a 50-line Python script, but this highlights how simple it is to achieve.

Of course there will also be nefarious uses of AI voices. Scams that use voice cloning have been getting easier every year, but they’ve been possible for many years with the right know-how. Just like we’re learning that we can no longer trust images and videos we see because they could easily have been AI-generated, we will all soon learn that someone who sounds like a family member urgently requesting money may just be a scammer using a voice-cloning tool.

We don’t expect scammers to follow our proposal: They’ll find a way no matter what. But that’s always true of security standards, and a rising tide lifts all boats. We think the bulk of the uses will be with popular voice APIs from major companies—and everyone should know that they’re talking with a robot.

This essay was written with Barath Raghavan, and originally appeared in IEEE Spectrum.

Posted on February 6, 2025 at 7:03 AM • View Comments

On Generative AI Security

Microsoft’s AI Red Team just published “Lessons from Red Teaming 100 Generative AI Products.” Their blog post lists “three takeaways,” but the eight lessons in the report itself are more useful:

  1. Understand what the system can do and where it is applied.
  2. You don’t have to compute gradients to break an AI system.
  3. AI red teaming is not safety benchmarking.
  4. Automation can help cover more of the risk landscape.
  5. The human element of AI red teaming is crucial.
  6. Responsible AI harms are pervasive but difficult to measure.
  7. LLMs amplify existing security risks and introduce new ones.
  8. The work of securing AI systems will never be complete.

Posted on February 5, 2025 at 7:03 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.