I’ve been using LLM tools like Claude, Cursor, and ChatGPT extensively in my security & engineering work for the past couple years. Not as a replacement for thinking—but they genuinely help me move faster through complex problems. If you’re a security analyst, SOC analyst, threat hunter or engineer who hasn’t found a rhythm with these tools yet, I’ll try to share what’s been working for me with the hope it helps you too. — Read More

#cyber

McKinsey & Company — the world’s most prestigious consulting firm — built an internal AI platform called Lilli for its 43,000+ employees. Lilli is a purpose-built system: chat, document analysis, RAG over decades of proprietary research, AI-powered search across 100,000+ internal documents. Launched in 2023, named after the first professional woman hired by the firm in 1945, adopted by over 70% of McKinsey, processing 500,000+ prompts a month.

So we decided to point our autonomous offensive agent at it. No credentials. No insider knowledge. And no human-in-the-loop. Just a domain name and a dream.

Within 2 hours, the agent had full read and write access to the entire production database. — Read More

The Dirty Secret of the SOC

Here is the nuclear option nobody in Silicon Valley wanted to talk about. For years, the cybersecurity industry has been a high stakes gambling ring built on a house of cards. You pay millions for “endpoint protection” and “zero trust” wrappers that essentially act as expensive digital duct tape. But what happens when the tape is no longer needed because the hole in the wall simply ceases to exist.

Anthropic just pressed the button.

On February 20, 2026, the AI industry stopped playing nice. With the launch of Claude Code Security, Anthropic didn’t just release another “assistant.” They released a predator. This isn’t the usual incremental update. This is a paradigm shift where the LLM moves from “writing buggy code” to “fixing bugs that have existed since the Clinton administration.” — Read More

For the last few months, we’ve been using the GitHub Security Lab Taskflow Agent along with a new set of auditing taskflows that specialize in finding web security vulnerabilities. They also turn out to be very successful at finding high-impact vulnerabilities in open source projects.

As security researchers, we’re used to losing time on possible vulnerabilities that turn out to be unexploitable, but with these new taskflows, we can now spend more of our time on manually verifying the results and sending out reports. Furthermore, the severity of the vulnerabilities that we’re reporting is uniformly high. Many of them are authorization bypasses or information disclosure vulnerabilities that allow one user to login as somebody else or to access the private data of another user.

Using these taskflows, we’ve reported more than 80 vulnerabilities so far.  — Read More

AI-native networks have been a recurring talking point at Mobile World Congress for years. What made MWC 2026 in Barcelona different was the evidence. A cascade of announcements from the world’s biggest telecom vendors, chipmakers, and operators didn’t just reiterate the vision for AI-RAN–they delivered field trial results, commercial product launches, open-source toolkits, and a multi-operator coalition committing to build 6G on AI-native foundations.

For enterprise and IT decision-makers, the signal is clear: the architectural shift happening in telecom infrastructure will soon reshape how connectivity is delivered, managed, and monetised. — Read More

Most agents today run generated code with full access to your secrets.

As more agents adopt coding agent patterns, where they read filesystems, run shell commands, and generate code, they’re becoming multi-component systems that each need a different level of trust.

While most teams run all of these components in a single security context, because that’s how the default tooling works, we recommend thinking about these security boundaries differently.

Below we walk through:
— The actors in agentic systems
— Where security boundaries should go between them
— An architecture for running agent and generated code in separate contexts

— Read More

Cloud-based alerting systems often struggle to distinguish between normal cloud activity and targeted malicious operations by known threat actors. The difficulty doesn’t lie in an inability to identify complex alerting operations across thousands of cloud resources or in a failure to follow identity resources, the problem lies in the accurate detection of known persistent threat actor group techniques specifically within cloud environments.

In this research, we hypothesize how a new method of alert analysis could be used to improve detection. Specifically, we look at cloud-based alerting events and their mapping to the MITRE ATT&CK^® tactics and techniques they represent. We believe that we can show a correlation between threat actors and the types of techniques they use, which will trigger specific types of alerting events within victim environments. This distinct, detectable pattern could be used to identify when a known threat actor group compromises an organization. — Read More

The title of the post is”What AI Security Research Looks Like When It Works,” and I agree:

In the latest OpenSSL security release> on January 27, 2026, twelve new zero-day vulnerabilities (meaning unknown to the maintainers at time of disclosure) were announced. Our AI system is responsible for the original discovery of all twelve, each found and responsibly disclosed to the OpenSSL team during the fall and winter of 2025. Of those, 10 were assigned CVE-2025 identifiers and 2 received CVE-2026 identifiers. Adding the 10 to the three we already found in the Fall 2025 release, AISLE is credited for surfacing 13 of 14 OpenSSL CVEs assigned in 2025, and 15 total across both releases. This is a historically unusual concentration for any single research team, let alone an AI-driven one. — Read More

Attacks against modern generative artificial intelligence (AI) large language models (LLMs) pose a real threat. Yet discussions around these attacks and their potential defenses are dangerously myopic. The dominant narrative focuses on “prompt injection,” a set of techniques to embed instructions into inputs to LLM intended to perform malicious activity. This term suggests a simple, singular vulnerability. This framing obscures a more complex and dangerous reality. Attacks on LLM-based systems have evolved into a distinct class of malware execution mechanisms, which we term “promptware.” In a new paper, we, the authors, propose a structured seven-step “promptware kill chain” to provide policymakers and security practitioners with the necessary vocabulary and framework to address the escalating AI threat landscape. — Read More

#cyber

State-sponsored hackers are exploiting highly-advanced tooling to accelerate their particular flavours of cyberattacks, with threat actors from Iran, North Korea, China, and Russia using models like Google’s Gemini to further their campaigns. They are able to craft sophisticated phishing campaigns and develop malware, according to a new report from Google’s Threat Intelligence Group (GTIG).

The quarterly AI Threat Tracker report, released today, reveals how government-backed attackers have begun to use artificial intelligence in the attack lifecycle – reconnaissance, social engineering, and eventually, malware development. This activity has become apparent thanks to the GTIG’s work during the final quarter of 2025.

“For government-backed threat actors, large language models have become essential tools for technical research, targeting, and the rapid generation of nuanced phishing lures,” GTIG researchers stated in their report. — Read More