Jeffrey P.

🚨🇦🇪 Telegram zero-day can enable code execution through malicious animated content A vulnerability in Telegram’s rlottie animation library can allow attackers to execute arbitrary code within the application process. Tracked as CVE-2026-111105, the flaw carries a CVSS score of 7.0 and was publicly disclosed by Trend Micro’s Zero Day Initiative after remaining unresolved at the client-code level. ⠀ The vulnerability is caused by improper validation of attacker-controlled animation data, resulting in a write before the beginning of an allocated memory buffer. ⠀ Despite reports describing the flaw as a zero-click sticker attack, ZDI’s final advisory says user interaction is required. The victim must visit a malicious page or open a malicious file for exploitation to occur. ⠀ Telegram told ZDI that server-side sticker validation mitigates the vulnerability. However, ZDI continued seeking a client-side fix and ultimately published the issue as a zero-day advisory on October 15. ⠀ No affected client versions, patched versions or confirmed in-the-wild attacks have been disclosed. Users should keep Telegram updated and avoid opening untrusted animated files, sticker packs or external links until a client-side fix is confirmed.

messaging my wife a link containing proof of her husband cheating on her from an anonymous instagram account and then when she clicks on it texting her to let her know she failed the family phishing test and she has to retake my cybersecurity course

Wow. I woke up to an impressive number of upset people who apparently don’t understand how LLMs work and can’t cope well with me pointing it out. An LLM is a statistical next-token prediction model. That’s how it works. It’s an extraordinarily sophisticated calculator performing math you could theoretically do by hand (if you were sufficiently masochistic). Math doesn’t know what it’s calculating. Pi doesn’t spend its evenings contemplating the nature of circles, and c doesn’t ponder the relationship between matter and energy. That doesn’t make the results any less accurate, impressive, or useful. Between inference requests, an LLM isn’t sitting around thinking. It isn’t reconsidering its answers, wondering whether it made a mistake, contemplating its existence, or questioning whether it’s conscious. Its weights remain fixed (it doesn’t learn), and computation resumes when it receives input. We know this because we designed these systems. The architectures, mathematics, implementations, and research are extensively documented. You can study them yourself. Of course, some people immediately lean into comparisons of biological intelligence or philosophical debates over consciousness because that is what they know. But biological intelligence and artificial neural networks are fundamentally different systems. Similar outputs don’t establish equivalent internal experiences. I experience consciousness directly. I infer that other humans do too because we share biological structures and processes. We don’t have equivalent evidence for an LLM, and nothing about its known operation gives evidence of conscious experience. Which brings me back to my original point. To me, understanding is conscious comprehension. It means being aware of information, recognizing its meaning, and experiencing that comprehension. If I wasn’t conscious I could not understand. We can perform actions unconsciously. We can produce correct results without knowing why they’re correct. But I distinguish those capabilities from consciously understanding what we’re doing. An LLM can solve complex problems, explain difficult concepts, and produce remarkably accurate information. I give it full credit for those capabilities. But producing an answer that we rationalize as understanding isn’t the same as experiencing understanding. You don’t have to agree with my definitions. We can debate functional understanding versus conscious comprehension all day, but that won’t establish whether an LLM actually experiences anything. The right debate is this… Could machines exhibit something different from human consciousness, something measurable, meaningful, and worth studying? I think so, absolutely! I’m entirely open to discussing “machine consciousness” in terms of observable, testable computational properties. We built these systems, we can instrument them, examine their internal mechanisms, and investigate what they’re capable of. That seems far more productive than endlessly arguing over whether a machine experiences life the way humans do. So here’s my question: How would you define machine consciousness, and more importantly, how would you measure it?