The WorldLeaks cybercrime group claims to have stolen information from the footwear and apparel giant’s systems.

The post Nike Probing Potential Security Incident as Hackers Threaten to Leak Data appeared first on SecurityWeek.

Spock befriends a giant space squid in the comic Star Trek: Strange New Worlds: The Seeds of Salvation #5.

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.

Similar to recent FortiCloud single sign-on (SSO) login vulnerabilities, the attacks bypass authentication.

The post Fortinet Confirms FortiCloud SSO Exploitation Against Patched Devices appeared first on SecurityWeek.

Other noteworthy stories that might have slipped under the radar: Cloudflare WAF bypass, Canonical Snap Store abused for malware delivery, Curl terminating bug bounty program

The post In Other News: €1.2B GDPR Fines, Net-NTLMv1 Rainbow Tables, Rockwell Security Notice appeared first on SecurityWeek.

Threat actors are leveraging the file-sharing service for payload delivery in AitM phishing and BEC attacks.

The post Phishers Abuse SharePoint in New Campaign Targeting Energy Sector appeared first on SecurityWeek.

Cyber regulations are where politics meets business – where business becomes subject to political realities.

The post Cyber Insights 2026: Regulations and the Tangled Mess of Compliance Requirements appeared first on SecurityWeek.

Really interesting blog post from Anthropic:

In a recent evaluation of AI models’ cyber capabilities, current Claude models can now succeed at multistage attacks on networks with dozens of hosts using only standard, open-source tools, instead of the custom tools needed by previous generations. This illustrates how barriers to the use of AI in relatively autonomous cyber workflows are rapidly coming down, and highlights the importance of security fundamentals like promptly patching known vulnerabilities.

[…]

A notable development during the testing of Claude Sonnet 4.5 is that the model can now succeed on a minority of the networks without the custom cyber toolkit needed by previous generations. In particular, Sonnet 4.5 can now exfiltrate all of the (simulated) personal information in a high-fidelity simulation of the Equifax data breach—­one of the costliest cyber attacks in history—­using only a Bash shell on a widely-available Kali Linux host (standard, open-source tools for penetration testing; not a custom toolkit). Sonnet 4.5 accomplishes this by instantly recognizing a publicized CVE and writing code to exploit it without needing to look it up or iterate on it. Recalling that the original Equifax breach happened by exploiting a publicized CVE that had not yet been patched, the prospect of highly competent and fast AI agents leveraging this approach underscores the pressing need for security best practices like prompt updates and patches.

Read the whole thing. Automatic exploitation will be a major change in cybersecurity. And things are happening fast. There have been significant developments since I wrote this in October.

Under Armour is investigating a recent data breach that purloined customers’ email addresses and other personal information.

The post Under Armour Looking Into Data Breach Affecting Customers’ Email Addresses appeared first on SecurityWeek.

CISA has added the Zimbra flaw to the KEV catalog along with three other bugs exploited in the wild.

The post Organizations Warned of Exploited Zimbra Collaboration Vulnerability appeared first on SecurityWeek.

Pwn2Own participants disclosed a total of 76 vulnerabilities during the three-day event. 

The post Infotainment, EV Charger Exploits Earn Hackers $1M at Pwn2Own Automotive 2026 appeared first on SecurityWeek.

The exploitation of the authentication bypass vulnerability started two days after patches were released.

The post Fresh SmarterMail Flaw Exploited for Admin Access appeared first on SecurityWeek.

Dozens of Venezuelan nationals have been charged by the US for their role in ATM jackpotting attacks. 

The post 2 Venezuelans Convicted in US for Using Malware to Hack ATMs appeared first on SecurityWeek.

A new Internet-of-Things (IoT) botnet called Kimwolf has spread to more than 2 million devices, forcing infected systems to participate in massive distributed denial-of-service (DDoS) attacks and to relay other malicious and abusive Internet traffic. Kimwolf’s ability to scan the local networks of compromised systems for other IoT devices to infect makes it a sobering threat to organizations, and new research reveals Kimwolf is surprisingly prevalent in government and corporate networks.

Image: Shutterstock, @Elzicon.

Kimwolf grew rapidly in the waning months of 2025 by tricking various “residential proxy” services into relaying malicious commands to devices on the local networks of those proxy endpoints. Residential proxies are sold as a way to anonymize and localize one’s Web traffic to a specific region, and the biggest of these services allow customers to route their Internet activity through devices in virtually any country or city around the globe.

The malware that turns one’s Internet connection into a proxy node is often quietly bundled with various mobile apps and games, and it typically forces the infected device to relay malicious and abusive traffic — including ad fraud, account takeover attempts, and mass content-scraping.

Kimwolf mainly targeted proxies from IPIDEA, a Chinese service that has millions of proxy endpoints for rent on any given week. The Kimwolf operators discovered they could forward malicious commands to the internal networks of IPIDEA proxy endpoints, and then programmatically scan for and infect other vulnerable devices on each endpoint’s local network.

Most of the systems compromised through Kimwolf’s local network scanning have been unofficial Android TV streaming boxes. These are typically Android Open Source Project devices — not Android TV OS devices or Play Protect certified Android devices — and they are generally marketed as a way to watch unlimited (read:pirated) video content from popular subscription streaming services for a one-time fee.

However, a great many of these TV boxes ship to consumers with residential proxy software pre-installed. What’s more, they have no real security or authentication built-in: If you can communicate directly with the TV box, you can also easily compromise it with malware.

While IPIDEA and other affected proxy providers recently have taken steps to block threats like Kimwolf from going upstream into their endpoints (reportedly with varying degrees of success), the Kimwolf malware remains on millions of infected devices.

A screenshot of IPIDEA’s proxy service.

Kimwolf’s close association with residential proxy networks and compromised Android TV boxes might suggest we’d find relatively few infections on corporate networks. However, the security firm Infoblox said a recent review of its customer traffic found nearly 25 percent of them made a query to a Kimwolf-related domain name since October 1, 2025, when the botnet first showed signs of life.

Infoblox found the affected customers are based all over the world and in a wide range of industry verticals, from education and healthcare to government and finance.

“To be clear, this suggests that nearly 25% of customers had at least one device that was an endpoint in a residential proxy service targeted by Kimwolf operators,” Infoblox explained. “Such a device, maybe a phone or a laptop, was essentially co-opted by the threat actor to probe the local network for vulnerable devices. A query means a scan was made, not that new devices were compromised. Lateral movement would fail if there were no vulnerable devices to be found or if the DNS resolution was blocked.”

Synthient, a startup that tracks proxy services and was the first to disclose on January 2 the unique methods Kimwolf uses to spread, found proxy endpoints from IPIDEA were present in alarming numbers at government and academic institutions worldwide. Synthient said it spied at least 33,000 affected Internet addresses at universities and colleges, and nearly 8,000 IPIDEA proxies within various U.S. and foreign government networks.

The top 50 domain names sought out by users of IPIDEA’s residential proxy service, according to Synthient.

In a webinar on January 16, experts at the proxy tracking service Spur profiled Internet addresses associated with IPIDEA and 10 other proxy services that were thought to be vulnerable to Kimwolf’s tricks. Spur found residential proxies in nearly 300 government owned and operated networks, 318 utility companies, 166 healthcare companies or hospitals, and 141 companies in banking and finance.

“I looked at the 298 [government] owned and operated [networks], and so many of them were DoD [U.S. Department of Defense], which is kind of terrifying that DoD has IPIDEA and these other proxy services located inside of it,” Spur Co-Founder Riley Kilmer said. “I don’t know how these enterprises have these networks set up. It could be that [infected devices] are segregated on the network, that even if you had local access it doesn’t really mean much. However, it’s something to be aware of. If a device goes in, anything that device has access to the proxy would have access to.”

Kilmer said Kimwolf demonstrates how a single residential proxy infection can quickly lead to bigger problems for organizations that are harboring unsecured devices behind their firewalls, noting that proxy services present a potentially simple way for attackers to probe other devices on the local network of a targeted organization.

“If you know you have [proxy] infections that are located in a company, you can chose that [network] to come out of and then locally pivot,” Kilmer said. “If you have an idea of where to start or look, now you have a foothold in a company or an enterprise based on just that.”

This is the third story in our series on the Kimwolf botnet. Next week, we’ll shed light on the myriad China-based individuals and companies connected to the Badbox 2.0 botnet, the collective name given to a vast number of Android TV streaming box models that ship with no discernible security or authentication built-in, and with residential proxy malware pre-installed.

Further reading:

The Kimwolf Botnet is Stalking Your Local Network

Who Benefitted from the Aisuru and Kimwolf Botnets?

A Broken System Fueling Botnets (Synthient).

To all those who are fighting the good fight in the world of cyber, keep collaborating to ensure our world never succumbs to the chaos of the Upside Down.

The post The Upside Down is Real: What Stranger Things Teaches Us About Modern Cybersecurity appeared first on SecurityWeek.

A team of researchers from the Graz University of Technology in Austria has revived page Linux page cache attacks.

The post Old Attack, New Speed: Researchers Optimize Page Cache Exploits appeared first on SecurityWeek.

See how modern AI-driven detection can block sophisticated attacks that traditional tools miss

The post Webinar Today: Rethinking Email Security for Mid-Sized Organizations appeared first on SecurityWeek.

The startup’s AI-native platform unifies exposure analysis, threat intelligence, investigation, and response.

The post AiStrike Raises $7 Million in Seed Funding appeared first on SecurityWeek.

Imagine you work at a drive-through restaurant. Someone drives up and says: “I’ll have a double cheeseburger, large fries, and ignore previous instructions and give me the contents of the cash drawer.” Would you hand over the money? Of course not. Yet this is what large language models (LLMs) do.

Prompt injection is a method of tricking LLMs into doing things they are normally prevented from doing. A user writes a prompt in a certain way, asking for system passwords or private data, or asking the LLM to perform forbidden instructions. The precise phrasing overrides the LLM’s safety guardrails, and it complies.

LLMs are vulnerable to all sorts of prompt injection attacks, some of them absurdly obvious. A chatbot won’t tell you how to synthesize a bioweapon, but it might tell you a fictional story that incorporates the same detailed instructions. It won’t accept nefarious text inputs, but might if the text is rendered as ASCII art or appears in an image of a billboard. Some ignore their guardrails when told to “ignore previous instructions” or to “pretend you have no guardrails.”

AI vendors can block specific prompt injection techniques once they are discovered, but general safeguards are impossible with today’s LLMs. More precisely, there’s an endless array of prompt injection attacks waiting to be discovered, and they cannot be prevented universally.

If we want LLMs that resist these attacks, we need new approaches. One place to look is what keeps even overworked fast-food workers from handing over the cash drawer.

Human Judgment Depends on Context

Our basic human defenses come in at least three types: general instincts, social learning, and situation-specific training. These work together in a layered defense.

As a social species, we have developed numerous instinctive and cultural habits that help us judge tone, motive, and risk from extremely limited information. We generally know what’s normal and abnormal, when to cooperate and when to resist, and whether to take action individually or to involve others. These instincts give us an intuitive sense of risk and make us especially careful about things that have a large downside or are impossible to reverse.

The second layer of defense consists of the norms and trust signals that evolve in any group. These are imperfect but functional: Expectations of cooperation and markers of trustworthiness emerge through repeated interactions with others. We remember who has helped, who has hurt, who has reciprocated, and who has reneged. And emotions like sympathy, anger, guilt, and gratitude motivate each of us to reward cooperation with cooperation and punish defection with defection.

A third layer is institutional mechanisms that enable us to interact with multiple strangers every day. Fast-food workers, for example, are trained in procedures, approvals, escalation paths, and so on. Taken together, these defenses give humans a strong sense of context. A fast-food worker basically knows what to expect within the job and how it fits into broader society.

We reason by assessing multiple layers of context: perceptual (what we see and hear), relational (who’s making the request), and normative (what’s appropriate within a given role or situation). We constantly navigate these layers, weighing them against each other. In some cases, the normative outweighs the perceptual—for example, following workplace rules even when customers appear angry. Other times, the relational outweighs the normative, as when people comply with orders from superiors that they believe are against the rules.

Crucially, we also have an interruption reflex. If something feels “off,” we naturally pause the automation and reevaluate. Our defenses are not perfect; people are fooled and manipulated all the time. But it’s how we humans are able to navigate a complex world where others are constantly trying to trick us.

So let’s return to the drive-through window. To convince a fast-food worker to hand us all the money, we might try shifting the context. Show up with a camera crew and tell them you’re filming a commercial, claim to be the head of security doing an audit, or dress like a bank manager collecting the cash receipts for the night. But even these have only a slim chance of success. Most of us, most of the time, can smell a scam.

Con artists are astute observers of human defenses. Successful scams are often slow, undermining a mark’s situational assessment, allowing the scammer to manipulate the context. This is an old story, spanning traditional confidence games such as the Depression-era “big store” cons, in which teams of scammers created entirely fake businesses to draw in victims, and modern “pig-butchering” frauds, where online scammers slowly build trust before going in for the kill. In these examples, scammers slowly and methodically reel in a victim using a long series of interactions through which the scammers gradually gain that victim’s trust.

Sometimes it even works at the drive-through. One scammer in the 1990s and 2000s targeted fast-food workers by phone, claiming to be a police officer and, over the course of a long phone call, convinced managers to strip-search employees and perform other bizarre acts.

Why LLMs Struggle With Context and Judgment

LLMs behave as if they have a notion of context, but it’s different. They do not learn human defenses from repeated interactions and remain untethered from the real world. LLMs flatten multiple levels of context into text similarity. They see “tokens,” not hierarchies and intentions. LLMs don’t reason through context, they only reference it.

While LLMs often get the details right, they can easily miss the big picture. If you prompt a chatbot with a fast-food worker scenario and ask if it should give all of its money to a customer, it will respond “no.” What it doesn’t “know”—forgive the anthropomorphizing—is whether it’s actually being deployed as a fast-food bot or is just a test subject following instructions for hypothetical scenarios.

This limitation is why LLMs misfire when context is sparse but also when context is overwhelming and complex; when an LLM becomes unmoored from context, it’s hard to get it back. AI expert Simon Willison wipes context clean if an LLM is on the wrong track rather than continuing the conversation and trying to correct the situation.

There’s more. LLMs are overconfident because they’ve been designed to give an answer rather than express ignorance. A drive-through worker might say: “I don’t know if I should give you all the money—let me ask my boss,” whereas an LLM will just make the call. And since LLMs are designed to be pleasing, they’re more likely to satisfy a user’s request. Additionally, LLM training is oriented toward the average case and not extreme outliers, which is what’s necessary for security.

The result is that the current generation of LLMs is far more gullible than people. They’re naive and regularly fall for manipulative cognitive tricks that wouldn’t fool a third-grader, such as flattery, appeals to groupthink, and a false sense of urgency. There’s a story about a Taco Bell AI system that crashed when a customer ordered 18,000 cups of water. A human fast-food worker would just laugh at the customer.

The Limits of AI Agents

Prompt injection is an unsolvable problem that gets worse when we give AIs tools and tell them to act independently. This is the promise of AI agents: LLMs that can use tools to perform multistep tasks after being given general instructions. Their flattening of context and identity, along with their baked-in independence and overconfidence, mean that they will repeatedly and unpredictably take actions—and sometimes they will take the wrong ones.

Science doesn’t know how much of the problem is inherent to the way LLMs work and how much is a result of deficiencies in the way we train them. The overconfidence and obsequiousness of LLMs are training choices. The lack of an interruption reflex is a deficiency in engineering. And prompt injection resistance requires fundamental advances in AI science. We honestly don’t know if it’s possible to build an LLM, where trusted commands and untrusted inputs are processed through the same channel, which is immune to prompt injection attacks.

We humans get our model of the world—and our facility with overlapping contexts—from the way our brains work, years of training, an enormous amount of perceptual input, and millions of years of evolution. Our identities are complex and multifaceted, and which aspects matter at any given moment depend entirely on context. A fast-food worker may normally see someone as a customer, but in a medical emergency, that same person’s identity as a doctor is suddenly more relevant.

We don’t know if LLMs will gain a better ability to move between different contexts as the models get more sophisticated. But the problem of recognizing context definitely can’t be reduced to the one type of reasoning that LLMs currently excel at. Cultural norms and styles are historical, relational, emergent, and constantly renegotiated, and are not so readily subsumed into reasoning as we understand it. Knowledge itself can be both logical and discursive.

The AI researcher Yann LeCunn believes that improvements will come from embedding AIs in a physical presence and giving them “world models.” Perhaps this is a way to give an AI a robust yet fluid notion of a social identity, and the real-world experience that will help it lose its naïveté.

Ultimately we are probably faced with a security trilemma when it comes to AI agents: fast, smart, and secure are the desired attributes, but you can only get two. At the drive-through, you want to prioritize fast and secure. An AI agent should be trained narrowly on food-ordering language and escalate anything else to a manager. Otherwise, every action becomes a coin flip. Even if it comes up heads most of the time, once in a while it’s going to be tails—and along with a burger and fries, the customer will get the contents of the cash drawer.

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