Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Join us at swampUP New York, September 1-3, for our joint session Trusted AI Delivery at Scale: Securing Every Artifact from Curation to Cloud, where we walk the full chain of custody from the moment a package enters your organization to the moment your agent runs on Amazon Bedrock AgentCore. Register here. AI agents are becoming real users of internal systems. They open pull requests, run queries, and pull and publish artifacts in repositories like JFrog Artifactory.

Somewhere in the last few months, the math of application security quietly broke. Anthropic’s Claude Mythos Preview didn’t just analyze code, it found a 27-year-old vulnerability in OpenBSD, a 16-year-old bug in FFmpeg, and a 17-year-old remote code execution flaw in FreeBSD, entirely on its own. Then it went further: it built working exploits for them. No human guidance. No months of manual research. And by Anthropic’s own account, this is only a preview of what’s coming.

Growing engineering organizations all hit the same wall. More teams shipping software means more repositories, more permission requests, more onboarding cycles, and eventually one platform admin fielding every change. The platform that was supposed to accelerate delivery has now become the bottleneck. JFrog Projects is built to break that pattern.

A bank isn’t just a vault holding money. It is an engine powered by implicit public trust, sustained by the continuous confidence that funds remain secure and accessible on demand. When operational risks fail, whether through cyber breaches, system outages, or third-party vulnerabilities, that trust shatters, threatening not just an individual institution, but the stability of the entire financial network. This is why regulatory oversight goes beyond standard compliance.

Ask most security tools what an AI coding agent just built, and they can tell you. Ask what it was allowed to consume before it started, and far fewer have an answer. That gap, between watching agentic development and controlling it, is what securing it actually comes down to. Securing agentic development means stopping risk before it enters a build, not flagging it after. And risk prevention has a prerequisite most approaches skip: you can only account for the assets you actually hold and manage.

A developer on your team just told Claude Code to connect to a new MCP server, the protocol coding agents use to reach organizational tools and data. Nobody in security reviewed it. Nobody in security even knows it happened. For two-thirds of enterprises, the primary obstacle to scaling agentic development isn’t budget or headcount — it’s security risk.

Just last week, OpenAI and Hugging Face jointly disclosed what may be the first incident of its kind: during an internal evaluation of frontier cyber capabilities, OpenAI’s models, running deliberately without production safeguards in an isolated research environment, autonomously discovered and employed chained vulnerabilities to escape its sandbox, reach the open internet, and extract evaluation answers from Hugging Face’s infrastructure.

The JFrog Security Research team has discovered and disclosed a typosquatted NuGet package named Newtonsoftt.Json.Net. Note the double t and the.Net suffix. This package has been masquerading as the popular Newtonsoft.Json library while quietly shipping a trojanized fork. The trojan rigs Digitain, an online betting platform, and in later generations, exfiltrates rigged round results to an attacker-controlled server, utilizing the header X-Seq-ApiKey: theperfectheist2025.

What is DevGovOps? DevGovOps is a Software Supply Chain Engineering practice that integrates continuous governance and compliance into the DevOps software delivery lifecycle. Rather than treating compliance as a retrospective, manual hurdle, DevGovOps ensures that policy enforcement, continuous auditability, and cryptographic traceability are natural outputs of every release.

AI agents and LLMs are already building, analyzing, and deploying code across your software development lifecycle. As software supply chains become increasingly AI-driven, proactive security and access controls are your only path to success. To effectively govern authentication and permissions without sacrificing development speed, you must update your access management strategies.