The malware includes four separate backdoors:

Creating four backdoors facilitates the attackers having multiple points of re-entry should one be detected and removed. A unique case we haven’t seen before. Which introduces another type of attack made possibly by abusing websites that don’t monitor 3rd party dependencies in the browser of their users.

The four backdoors:

The functions of the four backdoors are explained below:

• Backdoor 1, which uploads and installs a fake plugin named “Ultra SEO Processor,” which is then used to execute attacker-issued commands ...

Squid is a loyalty card platform in Ireland.

Blog moderation policy.

The EFF has created an open-source hardware tool to detect IMSI catchers: fake cell phone towers that are used for mass surveillance of an area.

It runs on a $20 mobile hotspot.

Interesting article—with photos!—of the US/UK “Combined Cipher Machine” from WWII.

Of the five, one is a Windows vulnerability, another is a Cisco vulnerability. We don’t have any details about who is exploiting them, or how.

News article. Slashdot thread.

This is a sad story of someone who downloaded a Trojaned AI tool that resulted in hackers taking over his computer and, ultimately, costing him his job.

Firefly squid is now a delicacy in New York.

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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...

Last month, the UK government demanded that Apple weaken the security of iCloud for users worldwide. On Friday, Apple took steps to comply for users in the United Kingdom. But the British law is written in a way that requires Apple to give its government access to anyone, anywhere in the world. If the government demands Apple weaken its security worldwide, it would increase everyone’s cyber-risk in an already dangerous world.

If you’re an iCloud user, you have the option of turning on something called “advanced data protection,” or ADP. In that mode, a majority of your data is end-to-end encrypted. This means that no one, not even anyone at Apple, can read that data. It’s a restriction enforced by mathematics—cryptography—and not policy. Even if someone successfully hacks iCloud, they can’t read ADP-protected data...

It looks like a very sophisticated attack against the Dubai-based exchange Bybit:

Bybit officials disclosed the theft of more than 400,000 ethereum and staked ethereum coins just hours after it occurred. The notification said the digital loot had been stored in a “Multisig Cold Wallet” when, somehow, it was transferred to one of the exchange’s hot wallets. From there, the cryptocurrency was transferred out of Bybit altogether and into wallets controlled by the unknown attackers.

[…]

…a subsequent investigation by Safe found no signs of unauthorized access to its infrastructure, no compromises of other Safe wallets, and no obvious vulnerabilities in the Safe codebase. As investigators continued to dig in, they finally settled on the true cause. Bybit ultimately said that the fraudulent transaction was “manipulated by a sophisticated attack that altered the smart contract logic and masked the signing interface, enabling the attacker to gain control of the ETH Cold Wallet.”...

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...