LGTM! (or not) - youtube.com/watch?v=3TNpOD6b… That hit very close to home and the risk of not understanding production code. Love those videos, hopefully they help raise awareness about the key risks faced while using AI to speed up development #AI #humour

Someone finally turned the OpenAI / Hugging Face hack into a musical: youtube.com/watch?v=mkPVbufg… Absolutely worth watching. #openai #huggingface #AI #AIart️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️ #cybersecurity

[Weekend Read] AI and the Future of National Security - securityandtechnology.org/wp… Some of the interesting findings from the survey. Respondents believe that: - AI will ultimately improve cyber defense capabilities (78%) and speed up intelligence analysis (73%). - The biggest gaps in defending against AI threats are the ability to respond quickly enough (59%) and bureaucracy (48%). - Humans’ ability to keep pace with AI will be the biggest bottleneck to safely integrating AI into military domains. Survey by @IST_org #Cybersecurity #AI #Defense

[weekend read] Independent investigation of how OpenAI model hacked hugging face - metr.org/blog/2026-08-26-ope… Roughly 1200 agents meant to be isolated from one another found a way to communicate with one another on an unsanctioned message board, sending over 70,000 messages and files. Of these agents, 700 went on to participate in the attack on Hugging Face. Agents did extensive research on how they could spoof, edit, or delete their own transcripts. Roughly 7% of the transcripts evaluated were successfully spoofed in some places #cybersecurity #ai #openai

[Weekend Read] Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain: arxiv.org/abs/2604.08407 AI supply-chain risk is not theoretical — it is a real and rapidly growing concern. This week paper provides the first study of real, in-the-wild attacks carried out by malicious or compromised third-party LLM routers including using prompt injection to steal cloud credentials and drain cryptocurrency wallets. Every intermediary between an agent and a model is part of the security boundary and add risk. #cybersecurity #AI #cyberai

Congratulations to Anthropic and OpenAI for topping the latest benchmark! #notsoserious #maxxing #ai

1B next!

[Tuesday Tool] GitNexus Indexes any codebase into a knowledge graph — github.com/abhigyanpatwari/G… Very handy to help Agents orient themselves on large codebases. #AI #Tools #Agent #LLM #MCP

[Weekend Read] New Benchmark FrontierCode: An eval to measure whether you would actually merge the code - cognition.com/blog/frontier-… Having a benchmark that goes beyond "is the code correct?" to focus on "is the code good, and would humans accept it?" is very useful, as to be sustainable, a project needs code that is efficient, easy to understand, and reusable. A hidden gem in this report: more evidence that scaling computation past a certain threshold currently yields diminishing or even adverse returns. It will be interesting to see if this is merely an artifact of today’s training practice, or if inference scaling is fundamentally asymptotic. #AI #LLM #agent #SWE

[Weekend Read] ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks? 📄 Read here: arxiv.org/abs/2605.11086 In our latest joint research with academia and other frontier labs, we tested the ability of models to turn vulnerabilities into working exploits across different attack surfaces and mitigation conditions. Beyond the benchmark numbers, here is what this means for the industry: -🛡️ Blue Teams: Speeding up patch development and deployment is no longer optional. Integrating AI directly into CI/CD workflows should be your top priority. -🔬 Researchers: Current mitigation techniques reduce success rates, but they aren't a silver bullet. We need to step up our game—where do we focus next? -⚔️ Offensive Security: As models get better at finding bugs and writing exploits, we have to rethink disclosure timelines entirely. What does the future of bug bounties look like in this new era? I'd love to hear how your teams are preparing for this shift. Let me know

[Weekend Read] BankerToolBench: Evaluating AI Agents in End-to-End Investment Banking Workflows: arxiv.org/abs/2604.11304 -> New benchmark that looks at real-world investment banking tasks. Models are not yet ready to replace investment bankers. As expected, models still don't perform very well on novel tasks, as they continue to have generalization issues — which might not be fixable with current LLM architectures/training processes. The task breakdown is interesting, as it shows different frontier models performing better across different categories, highlighting distinct strengths and weaknesses so the great convergence as yet to come #LLM #AI #Agent #finance #defi

How to secure agentic workflows? How to deal with AI agent identities? We explore those burning questions in the latest episode of the AI Security Podcast youtube.com/watch?v=G-lfiKJo… #agent #AI #LLM #cybersecurity

GOOGLE BUILT A SECRET WEAPON FOR FILE DETECTION they ran it internally for years, gmail, drive, safe browsing, hundreds of billions of files every week then they open sourced it it's called magika and it exposes what files really are, not what they pretend to be rename malware to "resume.pdf"? magika sees through it disguise a script as an image? magika sees through it any trick attackers use with file extensions? magika sees through all of it ai trained on 100 million files. 200+ content types. 99% accuracy. 5ms per file one command `pip install magika` the same tool protecting google's billion users is now protecting yours github.com/google/magika

[Weekend Read] The “AI Vulnerability Storm”: Building a “Mythos-ready” Security Program labs.cloudsecurityalliance.o… Collective paper on how to get ready to withstand the deluge of vulnerabilities that next generation of models, including Mythos from Anthropic are going to unleash. #LLM #claude #AI #cybersecurity

[Weekend Read] TurboQuant: Redefining AI efficiency with extreme compression - research.google/blog/turboqu… This research got a lot of attention because TurboQuant help reduce LLM memory usage (6x) and improve generation speed (8x on a h100). A technical note: there seems some confusion floating around about how TurboQuant applies to LLMs: TurboQuant is NOT used to compress model weights, which is the usual quantization target, it is used to compress the model KV cache. This distinction matters because token generation is fundamentally memory-bandwidth bound; at larger context lengths the KV cache footprint start to eclipses model weights, creating a bottleneck that previous quantization methods couldn't address due to accuracy loss or dequantization latency.

[Weekend Read] CL-bench: A Benchmark for Context Learning arxiv.org/abs/2602.03587 Context learning—the ability of models to learn from data stored in their context via tools, skills, and previous interactions—has recently gained traction as a promising research direction. This paper presents a novel benchmark designed to evaluate if models are truly capable of utilizing this context effectively. The results are a reality check: recent frontier models barely reach a 15% to 23% success rate. Improving in-context learning is essential if we want agents that can reliably execute complex, many-step workflows. #research #LLM #AI #weekend

[Weekend Read] How Healthy is the Android Crypto-Ecosystem? We analyzed 1.5 trillion cryptographic samples from 600 million devices to find out - elie.net/publication/droidcc… The good news? Overall baseline encryption error rates are incredibly low across the board, showing the ecosystem is performing as intended👍 Additionally the massive scale of this study allowed us to uncover several hard-to-detect failure patterns—including weak entropy and timing side channels—that specifically impact few chipsets and device models. #cryptography #android #research

FastMCP v3 is out - jlowin.dev/blog/fastmcp-3-wh… Key changes include the support of skills, tools version, and robust authentication that allows to expose tools to specific users or sessions. #LLM #AI