Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

SQL injections are still among us. On July 17, WordPress released an emergency security update. Version 7.0.2 fixes an unauthenticated remote code execution flaw in WordPress core that an anonymous attacker can trigger against a stock install with no plugins involved. If your site runs an affected version, update today. WordPress.org has turned on forced auto-updates for affected sites because of how severe this is. We are tracking this vulnerability in Aikido Intel.

TL;DR Every frontier model launch now comes with the same cybersecurity claim: it finds vulnerabilities. But does it work on a real bug in a real repository, or just on a curated example? Of the dozen models you could pick, which is worth trusting with code review? And since the strongest models cost ten times or more per run than the cheapest, what does that extra spend actually buy you in bugs found?

TL;DR: Aikido Intel is a real-time supply chain intelligence feed. It detects both malware and vulnerabilities in open-source ecosystems. Aikido's world-class researchers maintain our LLM-powered pipeline to find malware and validate the most malicious cases by hand. The vulnerability detection system monitors package changes across ecosystems to catch and document vulnerabilities that don’t have CVEs assigned.

Supply chain attacks are having a moment. Open-source malware detections jumped 73% in 2025. In the past year, the debug and chalk packages were backdoored, the tj-actions GitHub Action was compromised and pulled malicious code into thousands of pipelines, and the axios maintainer account was hijacked and used to distribute a RAT. Malicious releases also hit Zapier, ENS Domains, PostHog, and Bitwarden CLI. Every one of these attacks was preventable with controls that were available at the time.

It’s amazing how non-developers have recently been empowered to create their own apps that can even generate revenue. We’ve recently seen progress across the AI development field, from AI being successful in “greenfield code” (apps built from scratch) towards “brownfield code” (larger scale existing applications).

Pentesting made sense when releases happened every few months. A point-in-time assessment could provide an accurate picture of risk for weeks, sometimes months. Today, engineering teams ship continuously. Our State of AI in Pentesting survey of 200 CISOs and 200 engineering leaders, found that 76% deploy significant changes at least weekly, while nearly 40% deploy daily. Yet only 21% validate security on every release. That gap has consequences.

A malicious release of @injectivelabs/sdk-ts, an npm package that pulls around 50,000 weekly downloads, shipped code that records wallet mnemonics and private keys as they are derived and ships them to an attacker-controlled endpoint. The bad version, 1.20.21, was live on npm for under an hour on June 8, 2026 before the maintainer noticed and published a clean fix.