Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Sybil attacks are well documented in academic research. In practice, most organizations discover them too late, after the fake identities have already accumulated enough network influence to do real damage. The attack does not announce itself. It looks like growth. You see more nodes. More accounts. More participation. All of it is controlled by one attacker running a coordinated identity flood.

AI is changing how attacks happen, and how fast they happen. Seemplicity’s 2026 State of Exposure Management report shows why most security teams aren’t struggling to find risk, but to fix it quickly enough. Based on insights from 300 security leaders, it highlights where remediation breaks down, how AI is being used today, and why execution is becoming the real bottleneck.

Everyone is calling Claude Mythos a watershed moment. I’d like to offer a slightly different take. Not because the capability isn’t real, it is. But if Mythos is the moment that finally convinced your organization that rapid vulnerability discovery is an existential threat, you’ve been watching the wrong thing. We saw this coming. Vulnerability Management has been moving in this direction for years, and we built Nucleus with this trajectory in mind. What surprises me is the surprise.

Risk is expanding faster than most organizations can measure it, communicate it, and act on it. The convergence of AI, an ever-expanding attack surface, and deep, often hidden supply chain risks—extending into third-, fourth-, and fifth-party connections—all pose strategic and material risks to companies. Security leaders are ultimately looking for better ways to identify risk, prioritize action, and support stronger risk decisions across the entire business ecosystem.

On April 7, 2026, a security researcher described an Adobe Reader zero-day vulnerability that has been exploited since at least December 2025. The vulnerability allows threat actors to execute privileged Acrobat APIs via specially crafted malicious PDF files that execute obfuscated JavaScript when opened. Exploitation allows attackers to steal sensitive user and system data and to potentially launch additional attacks and remotely execute code.

Following our article on the challenges posed by agentic AI, we gave OpenClaw access to one of our legacy networks In my previous article on OpenClaw I wrote: “Even the most ‘risk-on’ organizations with deep AI and security experience, will likely find it challenging to configure OpenClaw in a way that effectively mitigates the risk of compromise or data loss, while still retaining any productivity value.” The Red Team here at Sophos took that as ‘challenge accepted’, s

We can't control the pace of AI-driven vulnerability discovery, but we can control how fast we respond. Last week, Thomas Ptacek published a piece arguing that vulnerability research is cooked. His thesis: AI agents are about to drown us in a steady stream of validated, exploitable, high-severity vulnerabilities, faster than anyone can patch them. But from where I sit, the more urgent question isn't whether the flood is coming, but whether the infrastructure we depend on can absorb it.

The software supply chain continues to face escalating threats, with malicious actors targeting developers and organizations at an unprecedented scale. In our Spring 2026 Threat Research Review, we analyze the latest trends, uncover alarming statistics, and highlight the evolving tactics used by attackers. From dependency injection attacks to the rise of typosquatting, this report provides a comprehensive look at the threats shaping the software ecosystem.

This week Anthropic announced Project Glasswing, a cybersecurity initiative built around Claude Mythos Preview, an unreleased frontier AI model capable of autonomously discovering and developing exploits for zero-day vulnerabilities across major operating systems and web browsers. According to early details, the model has already identified thousands of critical vulnerabilities that traditional tools have missed for years.

Backups are as good as the testing carried out to check effectiveness. Key aspects to consider when verifying GitHub backup effectiveness include: Has all data been covered and successfully backed up? Is the frequency of backup appropriate? Are recovery procedures correctly implemented? Read on as we shed more light on testing GitHub backups. Find out more about backup testing best practices, the issues that may arise, how to address them and why backup verification is so important.