Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Autonomous pentesting platforms are sitting at the top of HackerOne’s US leaderboard, surfacing zero-days in systems that had passed traditional audits for years. The capability is real, it is here, and it is only getting faster. But CISOs and procurement teams are not rushing to deploy it.

A supply chain attack is actively spreading through the npm registry by abusing a file most security tooling never looks at: binding.gyp. Instead of relying on the well-monitored preinstall or postinstall lifecycle scripts, the malware ships a weaponized binding.gyp that triggers node-gyp to execute attacker-controlled code automatically during npm install.

With code being written (& generated) faster than ever before, there is the unfortunate side effect that security vulnerabilities are also coming faster than ever before. Asking your LLM not to include security vulnerabilities in its code doesn't always work. It is becoming clear that the way software is built today, manually or with assistance, is insufficient when it comes to reliably, consistently, and provably writing secure code.

Most organizations spend their AI security budget on the wrong layer. The instinct is to just buy visibility to inventory the models, map the APIs, and ship a dashboard. But visibility alone won’t stop the coding agent that just pulled in a compromised MCP server. It won’t stop the production agent that’s about to forward a customer record to a place it shouldn’t go.

Businesses increasingly identify cyber risk as a core operational concern. Yet many cyber incidents still stem from basic, preventable vulnerabilities such as susceptibility to phishing, weak passwords, unpatched software and misconfigured systems. Insurers can play an important role in helping to raise firms’ cybersecurity hygiene and enhancing overall cyber resilience. However, cyber insurance penetration in certain market segments and regions remains low.

Let’s catch up on the more interesting vulnerability disclosures and cyber security news gathered from articles across the web this week. This is what we have been reading about on our coffee break! A heap of something simple can cause a bit of a headache it seems.

Walking the floor at Infosecurity Europe this week, it was impossible to avoid the subject of AI. Every conversation seemed to touch on it in some way. Vendors were demonstrating AI-powered detection capabilities, security teams were discussing governance frameworks, and practitioners were debating how best to secure the models, agents and data pipelines that are rapidly becoming part of everyday enterprise operations.

Bringing artificial intelligence into the office is a bit like adopting a hyper-energetic, brilliant, but chaotic intern. It can supercharge productivity, but if left unsupervised, it can accidentally delete the company database or invite a lawsuit. While the benefits of workplace AI are heavily advertised, deploying it without a safety net introduces significant vulnerabilities. Here’s a comprehensive breakdown of the risks businesses face when integrating AI into their daily operations.

There’s a persistent myth about red team operators: that the job is all zero-days, glowing terminals, and cinematic “I’m in” moments. The reality is more interesting and far more human. A day in the life of a red teamer is less about chasing flashy exploits and more about understanding how real people, real systems, and real environments fail under pressure.

NVIDIA NIM (NVIDIA Inference Microservices) packages production-ready AI models into optimized containers for enterprise deployment. Your developers need them. Your coding agents pull them. And until now, they pulled them directly from NVIDIA’s NGC registry, bypassing the supply chain controls you’ve spent years building. JFrog AI Catalog now brings NVIDIA NIM models under the same governance as every other artifact in your organization, with no separate registry and no governance gap.