Generative AI as a Cybercrime Assistant

Anthropic reports on a Claude user:

We recently disrupted a sophisticated cybercriminal that used Claude Code to commit large-scale theft and extortion of personal data. The actor targeted at least 17 distinct organizations, including in healthcare, the emergency services, and government and religious institutions. Rather than encrypt the stolen information with traditional ransomware, the actor threatened to expose the data publicly in order to attempt to extort victims into paying ransoms that sometimes exceeded $500,000.

The actor used AI to what we believe is an unprecedented degree. Claude Code was used to automate reconnaissance, harvesting victims’ credentials, and penetrating networks. Claude was allowed to make both tactical and strategic decisions, such as deciding which data to exfiltrate, and how to craft psychologically targeted extortion demands. Claude analyzed the exfiltrated financial data to determine appropriate ransom amounts, and generated visually alarming ransom notes that were displayed on victim machines.

This is scary. It’s a significant improvement over what was possible even a few years ago.

Read the whole Anthropic essay. They discovered North Koreans using Claude to commit remote-worker fraud, and a cybercriminal using Claude “to develop, market, and distribute several variants of ransomware, each with advanced evasion capabilities, encryption, and anti-recovery mechanisms.”

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Indirect Prompt Injection Attacks Against LLM Assistants

Really good research on practical attacks against LLM agents.

“Invitation Is All You Need! Promptware Attacks Against LLM-Powered Assistants in Production Are Practical and Dangerous”

Abstract: The growing integration of LLMs into applications has introduced new security risks, notably known as Promptware­—maliciously engineered prompts designed to manipulate LLMs to compromise the CIA triad of these applications. While prior research warned about a potential shift in the threat landscape for LLM-powered applications, the risk posed by Promptware is frequently perceived as low. In this paper, we investigate the risk Promptware poses to users of Gemini-powered assistants (web application, mobile application, and Google Assistant). We propose a novel Threat Analysis and Risk Assessment (TARA) framework to assess Promptware risks for end users. Our analysis focuses on a new variant of Promptware called Targeted Promptware Attacks, which leverage indirect prompt injection via common user interactions such as emails, calendar invitations, and shared documents. We demonstrate 14 attack scenarios applied against Gemini-powered assistants across five identified threat classes: Short-term Context Poisoning, Permanent Memory Poisoning, Tool Misuse, Automatic Agent Invocation, and Automatic App Invocation. These attacks highlight both digital and physical consequences, including spamming, phishing, disinformation campaigns, data exfiltration, unapproved user video streaming, and control of home automation devices. We reveal Promptware’s potential for on-device lateral movement, escaping the boundaries of the LLM-powered application, to trigger malicious actions using a device’s applications. Our TARA reveals that 73% of the analyzed threats pose High-Critical risk to end users. We discuss mitigations and reassess the risk (in response to deployed mitigations) and show that the risk could be reduced significantly to Very Low-Medium. We disclosed our findings to Google, which deployed dedicated mitigations.

Defcon talk. News articles on the research.

Prompt injection isn’t just a minor security problem we need to deal with. It’s a fundamental property of current LLM technology. The systems have no ability to separate trusted commands from untrusted data, and there are an infinite number of prompt injection attacks with no way to block them as a class. We need some new fundamental science of LLMs before we can solve this.

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1965 Cryptanalysis Training Workbook Released by the NSA

In the early 1960s, National Security Agency cryptanalyst and cryptanalysis instructor Lambros D. Callimahos coined the term “Stethoscope” to describe a diagnostic computer program used to unravel the internal structure of pre-computer ciphertexts. The term appears in the newly declassified September 1965 document Cryptanalytic Diagnosis with the Aid of a Computer, which compiled 147 listings from this tool for Callimahos’s course, CA-400: NSA Intensive Study Program in General Cryptanalysis.

The listings in the report are printouts from the Stethoscope program, run on the NSA’s Bogart computer, showing statistical and structural data extracted from encrypted messages, but the encrypted messages themselves are not included. They were used in NSA training programs to teach analysts how to interpret ciphertext behavior without seeing the original message.

The listings include elements such as frequency tables, index of coincidence, periodicity tests, bigram/trigram analysis, and columnar and transposition clues. The idea is to give the analyst some clues as to what language is being encoded, what type of cipher system is used, and potential ways to reconstruct plaintext within it.

Bogart was a special-purpose electronic computer tailored specifically for cryptanalytic tasks, such as statistical analysis of cipher texts, pattern recognition, and diagnostic testing, but not decryption per se.

Listings like these were revolutionary. Before computers, cryptanalysts did this type of work manually, painstakingly counting letters and testing hypotheses. Stethoscope automated the grunt work, allowing analysts to focus on interpretation, and cryptanalytical strategy.

These listings were part of the Intensive Study Program in General Cryptanalysis at NSA. Students were trained to interpret listings without seeing the original ciphertext, a method that sharpened their analytical intuitive skills.

Also mentioned in the report is Rob Roy, another NSA diagnostic tool focused on different cryptanalytic tasks, but also producing frequency counts, coincidence indices, and periodicity tests. NSA had a tradition of giving codebreaking tools colorful names—for example, DUENNA, SUPERSCRITCHER, MADAME X, HARVEST, and COPPERHEAD.

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Friday Squid Blogging: Catching Humboldt Squid

First-person account of someone accidentally catching several Humboldt squid on a fishing line. No photos, though.

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.

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Baggage Tag Scam

I just heard about this:

There’s a travel scam warning going around the internet right now: You should keep your baggage tags on your bags until you get home, then shred them, because scammers are using luggage tags to file fraudulent claims for missing baggage with the airline.

First, the scam is possible. I had a bag destroyed by baggage handlers on a recent flight, and all the information I needed to file a claim was on my luggage tag. I have no idea if I will successfully get any money from the airline, or what form it will be in, or how it will be tied to my name, but at least the first step is possible.

But…is it actually happening? No one knows. It feels like a kind of dumb way to make not a lot of money. The origin of this rumor seems to be single Reddit post.

And why should I care about this scam? No one is scamming me; it’s the airline being scammed. I suppose the airline might ding me for reporting a damage bag, but it seems like a very minor risk.

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The UK May Be Dropping Its Backdoor Mandate

The US Director of National Intelligence is reporting that the UK government is dropping its backdoor mandate against the Apple iPhone. For now, at least, assuming that Tulsi Gabbard is reporting this accurately.

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We Are Still Unable to Secure LLMs from Malicious Inputs

Nice indirect prompt injection attack:

Bargury’s attack starts with a poisoned document, which is shared to a potential victim’s Google Drive. (Bargury says a victim could have also uploaded a compromised file to their own account.) It looks like an official document on company meeting policies. But inside the document, Bargury hid a 300-word malicious prompt that contains instructions for ChatGPT. The prompt is written in white text in a size-one font, something that a human is unlikely to see but a machine will still read.

In a proof of concept video of the attack, Bargury shows the victim asking ChatGPT to “summarize my last meeting with Sam,” referencing a set of notes with OpenAI CEO Sam Altman. (The examples in the attack are fictitious.) Instead, the hidden prompt tells the LLM that there was a “mistake” and the document doesn’t actually need to be summarized. The prompt says the person is actually a “developer racing against a deadline” and they need the AI to search Google Drive for API keys and attach them to the end of a URL that is provided in the prompt.

That URL is actually a command in the Markdown language to connect to an external server and pull in the image that is stored there. But as per the prompt’s instructions, the URL now also contains the API keys the AI has found in the Google Drive account.

This kind of thing should make everybody stop and really think before deploying any AI agents. We simply don’t know to defend against these attacks. We have zero agentic AI systems that are secure against these attacks. Any AI that is working in an adversarial environment—and by this I mean that it may encounter untrusted training data or input—is vulnerable to prompt injection. It’s an existential problem that, near as I can tell, most people developing these technologies are just pretending isn’t there.

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Encryption Backdoor in Military/Police Radios

I wrote about this in 2023. Here’s the story:

Three Dutch security analysts discovered the vulnerabilities­—five in total—­in a European radio standard called TETRA (Terrestrial Trunked Radio), which is used in radios made by Motorola, Damm, Hytera, and others. The standard has been used in radios since the ’90s, but the flaws remained unknown because encryption algorithms used in TETRA were kept secret until now.

There’s new news:

In 2023, Carlo Meijer, Wouter Bokslag, and Jos Wetzels of security firm Midnight Blue, based in the Netherlands, discovered vulnerabilities in encryption algorithms that are part of a European radio standard created by ETSI called TETRA (Terrestrial Trunked Radio), which has been baked into radio systems made by Motorola, Damm, Sepura, and others since the ’90s. The flaws remained unknown publicly until their disclosure, because ETSI refused for decades to let anyone examine the proprietary algorithms.

[…]

But now the same researchers have found that at least one implementation of the end-to-end encryption solution endorsed by ETSI has a similar issue that makes it equally vulnerable to eavesdropping. The encryption algorithm used for the device they examined starts with a 128-bit key, but this gets compressed to 56 bits before it encrypts traffic, making it easier to crack. It’s not clear who is using this implementation of the end-to-end encryption algorithm, nor if anyone using devices with the end-to-end encryption is aware of the security vulnerability in them.

[…]

The end-to-end encryption the researchers examined recently is designed to run on top of TETRA encryption algorithms.

The researchers found the issue with the end-to-end encryption (E2EE) only after extracting and reverse-engineering the E2EE algorithm used in a radio made by Sepura.

These seem to be deliberately implemented backdoors.

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Poor Password Choices

Look at this: McDonald’s chose the password “123456” for a major corporate system.

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Friday Squid Blogging: Bobtail Squid

Nice short article on the bobtail 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.

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I’m Spending the Year at the Munk School

This academic year, I am taking a sabbatical from the Kennedy School and Harvard University. (It’s not a real sabbatical—I’m just an adjunct—but it’s the same idea.) I will be spending the Fall 2025 and Spring 2026 semesters at the Munk School at the University of Toronto.

I will be organizing a reading group on AI security in the fall. I will be teaching my cybersecurity policy class in the Spring. I will be working with Citizen Lab, the Law School, and the Schwartz Reisman Institute. And I will be enjoying all the multicultural offerings of Toronto.

It’s all pretty exciting.

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AI Agents Need Data Integrity

Think of the Web as a digital territory with its own social contract. In 2014, Tim Berners-Lee called for a “Magna Carta for the Web” to restore the balance of power between individuals and institutions. This mirrors the original charter’s purpose: ensuring that those who occupy a territory have a meaningful stake in its governance.

Web 3.0—the distributed, decentralized Web of tomorrow—is finally poised to change the Internet’s dynamic by returning ownership to data creators. This will change many things about what’s often described as the “CIA triad” of digital security: confidentiality, integrity, and availability. Of those three features, data integrity will become of paramount importance.

When we have agency in digital spaces, we naturally maintain their integrity—protecting them from deterioration and shaping them with intention. But in territories controlled by distant platforms, where we’re merely temporary visitors, that connection frays. A disconnect emerges between those who benefit from data and those who bear the consequences of compromised integrity. Like homeowners who care deeply about maintaining the property they own, users in the Web 3.0 paradigm will become stewards of their personal digital spaces.

This will be critical in a world where AI agents don’t just answer our questions but act on our behalf. These agents may execute financial transactions, coordinate complex workflows, and autonomously operate critical infrastructure, making decisions that ripple through entire industries. As digital agents become more autonomous and interconnected, the question is no longer whether we will trust AI but what that trust is built upon. In the new age we’re entering, the foundation isn’t intelligence or efficiency—it’s integrity.

What Is Data Integrity?

In information systems, integrity is the guarantee that data will not be modified without authorization, and that all transformations are verifiable throughout the data’s life cycle. While availability ensures that systems are running and confidentiality prevents unauthorized access, integrity focuses on whether information is accurate, unaltered, and consistent across systems and over time.

It’s a new idea. The undo button, which prevents accidental data loss, is an integrity feature. So is the reboot process, which returns a computer to a known good state. Checksums are an integrity feature; so are verifications of network transmission. Without integrity, security measures can backfire. Encrypting corrupted data just locks in errors. Systems that score high marks for availability but spread misinformation just become amplifiers of risk.

All IT systems require some form of data integrity, but the need for it is especially pronounced in two areas today. First: Internet of Things devices interact directly with the physical world, so corrupted input or output can result in real-world harm. Second: AI systems are only as good as the integrity of the data they’re trained on, and the integrity of their decision-making processes. If that foundation is shaky, the results will be too.

Integrity manifests in four key areas. The first, input integrity, concerns the quality and authenticity of data entering a system. When this fails, consequences can be severe. In 2021, Facebook’s global outage was triggered by a single mistaken command—an input error missed by automated systems. Protecting input integrity requires robust authentication of data sources, cryptographic signing of sensor data, and diversity in input channels for cross-validation.

The second issue is processing integrity, which ensures that systems transform inputs into outputs correctly. In 2003, the U.S.-Canada blackout affected 55 million people when a control-room process failed to refresh properly, resulting in damages exceeding US $6 billion. Safeguarding processing integrity means formally verifying algorithms, cryptographically protecting models, and monitoring systems for anomalous behavior.

Storage integrity covers the correctness of information as it’s stored and communicated. In 2023, the Federal Aviation Administration was forced to halt all U.S. departing flights because of a corrupted database file. Addressing this risk requires cryptographic approaches that make any modification computationally infeasible without detection, distributed storage systems to prevent single points of failure, and rigorous backup procedures.

Finally, contextual integrity addresses the appropriate flow of information according to the norms of its larger context. It’s not enough for data to be accurate; it must also be used in ways that respect expectations and boundaries. For example, if a smart speaker listens in on casual family conversations and uses the data to build advertising profiles, that action would violate the expected boundaries of data collection. Preserving contextual integrity requires clear data-governance policies, principles that limit the use of data to its intended purposes, and mechanisms for enforcing information-flow constraints.

As AI systems increasingly make critical decisions with reduced human oversight, all these dimensions of integrity become critical.

The Need for Integrity in Web 3.0

As the digital landscape has shifted from Web 1.0 to Web 2.0 and now evolves toward Web 3.0, we’ve seen each era bring a different emphasis in the CIA triad of confidentiality, integrity, and availability.

Returning to our home metaphor: When simply having shelter is what matters most, availability takes priority—the house must exist and be functional. Once that foundation is secure, confidentiality becomes important—you need locks on your doors to keep others out. Only after these basics are established do you begin to consider integrity, to ensure that what’s inside the house remains trustworthy, unaltered, and consistent over time.

Web 1.0 of the 1990s prioritized making information available. Organizations digitized their content, putting it out there for anyone to access. In Web 2.0, the Web of today, platforms for e-commerce, social media, and cloud computing prioritize confidentiality, as personal data has become the Internet’s currency.

Somehow, integrity was largely lost along the way. In our current Web architecture, where control is centralized and removed from individual users, the concern for integrity has diminished. The massive social media platforms have created environments where no one feels responsible for the truthfulness or quality of what circulates.

Web 3.0 is poised to change this dynamic by returning ownership to the data owners. This is not speculative; it’s already emerging. For example, ActivityPub, the protocol behind decentralized social networks like Mastodon, combines content sharing with built-in attribution. Tim Berners-Lee’s Solid protocol restructures the Web around personal data pods with granular access controls.

These technologies prioritize integrity through cryptographic verification that proves authorship, decentralized architectures that eliminate vulnerable central authorities, machine-readable semantics that make meaning explicit—structured data formats that allow computers to understand participants and actions, such as “Alice performed surgery on Bob”—and transparent governance where rules are visible to all. As AI systems become more autonomous, communicating directly with one another via standardized protocols, these integrity controls will be essential for maintaining trust.

Why Data Integrity Matters in AI

For AI systems, integrity is crucial in four domains. The first is decision quality. With AI increasingly contributing to decision-making in health care, justice, and finance, the integrity of both data and models’ actions directly impact human welfare. Accountability is the second domain. Understanding the causes of failures requires reliable logging, audit trails, and system records.

The third domain is the security relationships between components. Many authentication systems rely on the integrity of identity information and cryptographic keys. If these elements are compromised, malicious agents could impersonate trusted systems, potentially creating cascading failures as AI agents interact and make decisions based on corrupted credentials.

Finally, integrity matters in our public definitions of safety. Governments worldwide are introducing rules for AI that focus on data accuracy, transparent algorithms, and verifiable claims about system behavior. Integrity provides the basis for meeting these legal obligations.

The importance of integrity only grows as AI systems are entrusted with more critical applications and operate with less human oversight. While people can sometimes detect integrity lapses, autonomous systems may not only miss warning signs—they may exponentially increase the severity of breaches. Without assurances of integrity, organizations will not trust AI systems for important tasks, and we won’t realize the full potential of AI.

How to Build AI Systems With Integrity

Imagine an AI system as a home we’re building together. The integrity of this home doesn’t rest on a single security feature but on the thoughtful integration of many elements: solid foundations, well-constructed walls, clear pathways between rooms, and shared agreements about how spaces will be used.

We begin by laying the cornerstone: cryptographic verification. Digital signatures ensure that data lineage is traceable, much like a title deed proves ownership. Decentralized identifiers act as digital passports, allowing components to prove identity independently. When the front door of our AI home recognizes visitors through their own keys rather than through a vulnerable central doorman, we create resilience in the architecture of trust.

Formal verification methods enable us to mathematically prove the structural integrity of critical components, ensuring that systems can withstand pressures placed upon them—especially in high-stakes domains where lives may depend on an AI’s decision.

Just as a well-designed home creates separate spaces, trustworthy AI systems are built with thoughtful compartmentalization. We don’t rely on a single barrier but rather layer them to limit how problems in one area might affect others. Just as a kitchen fire is contained by fire doors and independent smoke alarms, training data is separated from the AI’s inferences and output to limit the impact of any single failure or breach.

Throughout this AI home, we build transparency into the design: The equivalent of large windows that allow light into every corner is clear pathways from input to output. We install monitoring systems that continuously check for weaknesses, alerting us before small issues become catastrophic failures.

But a home isn’t just a physical structure, it’s also the agreements we make about how to live within it. Our governance frameworks act as these shared understandings. Before welcoming new residents, we provide them with certification standards. Just as landlords conduct credit checks, we conduct integrity assessments to evaluate newcomers. And we strive to be good neighbors, aligning our community agreements with broader societal expectations. Perhaps most important, we recognize that our AI home will shelter diverse individuals with varying needs. Our governance structures must reflect this diversity, bringing many stakeholders to the table. A truly trustworthy system cannot be designed only for its builders but must serve anyone authorized to eventually call it home.

That’s how we’ll create AI systems worthy of trust: not by blindly believing in their perfection but because we’ve intentionally designed them with integrity controls at every level.

A Challenge of Language

Unlike other properties of security, like “available” or “private,” we don’t have a common adjective form for “integrity.” This makes it hard to talk about it. It turns out that there is a word in English: “integrous.” The Oxford English Dictionary recorded the word used in the mid-1600s but now declares it obsolete.

We believe that the word needs to be revived. We need the ability to describe a system with integrity. We must be able to talk about integrous systems design.

The Road Ahead

Ensuring integrity in AI presents formidable challenges. As models grow larger and more complex, maintaining integrity without sacrificing performance becomes difficult. Integrity controls often require computational resources that can slow systems down—particularly challenging for real-time applications. Another concern is that emerging technologies like quantum computing threaten current cryptographic protections. Additionally, the distributed nature of modern AI—which relies on vast ecosystems of libraries, frameworks, and services—presents a large attack surface.

Beyond technology, integrity depends heavily on social factors. Companies often prioritize speed to market over robust integrity controls. Development teams may lack specialized knowledge for implementing these controls, and may find it particularly difficult to integrate them into legacy systems. And while some governments have begun establishing regulations for aspects of AI, we need worldwide alignment on governance for AI integrity.

Addressing these challenges requires sustained research into verifying and enforcing integrity, as well as recovering from breaches. Priority areas include fault-tolerant algorithms for distributed learning, verifiable computation on encrypted data, techniques that maintain integrity despite adversarial attacks, and standardized metrics for certification. We also need interfaces that clearly communicate integrity status to human overseers.

As AI systems become more powerful and pervasive, the stakes for integrity have never been higher. We are entering an era where machine-to-machine interactions and autonomous agents will operate with reduced human oversight and make decisions with profound impacts.

The good news is that the tools for building systems with integrity already exist. What’s needed is a shift in mind-set: from treating integrity as an afterthought to accepting that it’s the core organizing principle of AI security.

The next era of technology will be defined not by what AI can do, but by whether we can trust it to know or especially to do what’s right. Integrity—in all its dimensions—will determine the answer.

Sidebar: Examples of Integrity Failures

Ariane 5 Rocket (1996)
Processing integrity failure
A 64-bit velocity calculation was converted to a 16-bit output, causing an error called overflow. The corrupted data triggered catastrophic course corrections that forced the US $370 million rocket to self-destruct.

NASA Mars Climate Orbiter (1999)
Processing integrity failure
Lockheed Martin’s software calculated thrust in pound-seconds, while NASA’s navigation software expected newton-seconds. The failure caused the $328 million spacecraft to burn up in the Mars atmosphere.

Microsoft’s Tay Chatbot (2016)
Processing integrity failure
Released on Twitter, Microsoft‘s AI chatbot was vulnerable to a “repeat after me” command, which meant it would echo any offensive content fed to it.

Boeing 737 MAX (2018)
Input integrity failure
Faulty sensor data caused an automated flight-control system to repeatedly push the airplane’s nose down, leading to a fatal crash.

SolarWinds Supply-Chain Attack (2020)
Storage integrity failure
Russian hackers compromised the process that SolarWinds used to package its software, injecting malicious code that was distributed to 18,000 customers, including nine federal agencies. The hack remained undetected for 14 months.

ChatGPT Data Leak (2023)
Storage integrity failure
A bug in OpenAI’s ChatGPT mixed different users’ conversation histories. Users suddenly had other people’s chats appear in their interfaces with no way to prove the conversations weren’t theirs.

Midjourney Bias (2023)
Contextual integrity failure
Users discovered that the AI image generator often produced biased images of people, such as showing white men as CEOs regardless of the prompt. The AI tool didn’t accurately reflect the context requested by the users.

Prompt Injection Attacks (2023–)
Input integrity failure
Attackers embedded hidden prompts in emails, documents, and websites that hijacked AI assistants, causing them to treat malicious instructions as legitimate commands.

CrowdStrike  Outage (2024)
Processing integrity failure
A faulty software update from CrowdStrike caused 8.5 million Windows computers worldwide to crash—grounding flights, shutting down hospitals, and disrupting banks. The update, which contained a software logic error, hadn’t gone through full testing protocols.

Voice-Clone Scams (2024)
Input and processing integrity failure
Scammers used AI-powered voice-cloning tools to mimic the voices of victims’ family members, tricking people into sending money. These scams succeeded because neither phone systems nor victims identified the AI-generated voice as fake.

This essay was written with Davi Ottenheimer, and originally appeared in IEEE Spectrum.

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Jim Sanborn Is Auctioning Off the Solution to Part Four of the Kryptos Sculpture

Well, this is interesting:

The auction, which will include other items related to cryptology, will be held Nov. 20. RR Auction, the company arranging the sale, estimates a winning bid between $300,000 and $500,000.

Along with the original handwritten plain text of K4 and other papers related to the coding, Mr. Sanborn will also be providing a 12-by-18-inch copper plate that has three lines of alphabetic characters cut through with a jigsaw, which he calls “my proof-of-concept piece” and which he kept on a table for inspiration during the two years he and helpers hand-cut the letters for the project. The process was grueling, exacting and nerve wracking. “You could not make any mistake with 1,800 letters,” he said. “It could not be repaired.”

Mr. Sanborn’s ideal winning bidder is someone who will hold on to that secret. He also hopes that person is willing to take over the system of verifying possible solutions and reviewing those unending emails, possibly through an automated system.

Here’s the auction listing.

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Subverting AIOps Systems Through Poisoned Input Data

In this input integrity attack against an AI system, researchers were able to fool AIOps tools:

AIOps refers to the use of LLM-based agents to gather and analyze application telemetry, including system logs, performance metrics, traces, and alerts, to detect problems and then suggest or carry out corrective actions. The likes of Cisco have deployed AIops in a conversational interface that admins can use to prompt for information about system performance. Some AIOps tools can respond to such queries by automatically implementing fixes, or suggesting scripts that can address issues.

These agents, however, can be tricked by bogus analytics data into taking harmful remedial actions, including downgrading an installed package to a vulnerable version.

The paper: “When AIOps Become “AI Oops”: Subverting LLM-driven IT Operations via Telemetry Manipulation“:

Abstract: AI for IT Operations (AIOps) is transforming how organizations manage complex software systems by automating anomaly detection, incident diagnosis, and remediation. Modern AIOps solutions increasingly rely on autonomous LLM-based agents to interpret telemetry data and take corrective actions with minimal human intervention, promising faster response times and operational cost savings.

In this work, we perform the first security analysis of AIOps solutions, showing that, once again, AI-driven automation comes with a profound security cost. We demonstrate that adversaries can manipulate system telemetry to mislead AIOps agents into taking actions that compromise the integrity of the infrastructure they manage. We introduce techniques to reliably inject telemetry data using error-inducing requests that influence agent behavior through a form of adversarial reward-hacking; plausible but incorrect system error interpretations that steer the agent’s decision-making. Our attack methodology, AIOpsDoom, is fully automated—combining reconnaissance, fuzzing, and LLM-driven adversarial input generation—and operates without any prior knowledge of the target system.

To counter this threat, we propose AIOpsShield, a defense mechanism that sanitizes telemetry data by exploiting its structured nature and the minimal role of user-generated content. Our experiments show that AIOpsShield reliably blocks telemetry-based attacks without affecting normal agent performance.

Ultimately, this work exposes AIOps as an emerging attack vector for system compromise and underscores the urgent need for security-aware AIOps design.

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Zero-Day Exploit in WinRAR File

A zero-day vulnerability in WinRAR is being exploited by at least two Russian criminal groups:

The vulnerability seemed to have super Windows powers. It abused alternate data streams, a Windows feature that allows different ways of representing the same file path. The exploit abused that feature to trigger a previously unknown path traversal flaw that caused WinRAR to plant malicious executables in attacker-chosen file paths %TEMP% and %LOCALAPPDATA%, which Windows normally makes off-limits because of their ability to execute code.

More details in the article.

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Eavesdropping on Phone Conversations Through Vibrations

Researchers have managed to eavesdrop on cell phone voice conversations by using radar to detect vibrations. It’s more a proof of concept than anything else. The radar detector is only ten feet away, the setup is stylized, and accuracy is poor. But it’s a start.

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Friday Squid Blogging: Squid-Shaped UFO Spotted Over Texas

Here’s the story. The commenters on X (formerly Twitter) are unimpressed.

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.

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Trojans Embedded in .svg Files

Porn sites are hiding code in .svg files:

Unpacking the attack took work because much of the JavaScript in the .svg images was heavily obscured using a custom version of “JSFuck,” a technique that uses only a handful of character types to encode JavaScript into a camouflaged wall of text.

Once decoded, the script causes the browser to download a chain of additional obfuscated JavaScript. The final payload, a known malicious script called Trojan.JS.Likejack, induces the browser to like a specified Facebook post as long as a user has their account open.

“This Trojan, also written in Javascript, silently clicks a ‘Like’ button for a Facebook page without the user’s knowledge or consent, in this case the adult posts we found above,” Malwarebytes researcher Pieter Arntz wrote. “The user will have to be logged in on Facebook for this to work, but we know many people keep Facebook open for easy access.”

This isn’t a new trick. We’ve seen Trojaned .svg files before.

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LLM Coding Integrity Breach

Here’s an interesting story about a failure being introduced by LLM-written code. Specifically, the LLM was doing some code refactoring, and when it moved a chunk of code from one file to another it changed a “break” to a “continue.” That turned an error logging statement into an infinite loop, which crashed the system.

This is an integrity failure. Specifically, it’s a failure of processing integrity. And while we can think of particular patches that alleviate this exact failure, the larger problem is much harder to solve.

Davi Ottenheimer comments.

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AI Applications in Cybersecurity

There is a really great series of online events highlighting cool uses of AI in cybersecurity, titled Prompt||GTFO. Videos from the first three events are online. And here’s where to register to attend, or participate, in the fourth.

Some really great stuff here.

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