Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Initially published by the Open Worldwide Application Security Project (OWASP) in 2023, the Top 10 for LLM Application Security list seeks to bridge the gap between traditional application security and the unique threats related to large language models (LLMs). Even where the vulnerabilities listed have the same names, the Top 10 for LLM Application Security focuses on how threat actors can exploit LLMs in new ways and potential remediation strategies that developers can implement.

Security teams deploying AI agents into Kubernetes know they need behavioral baselines. The concept is straightforward: define what “normal” looks like for each agent, then detect when behavior drifts in ways that suggest compromise. The problem is that AI agents are designed to change. A model update alters inference latency. A prompt revision shifts tool-calling sequences. A new MCP integration adds API destinations nobody flagged during the last security review.

A platform security engineer gets an alert at 2:14 a.m. One of the LangChain agents running in their production Kubernetes cluster has produced an execution graph with eleven nodes, seven tool calls, and an egress edge to a domain that is not in the agent’s approved integration list. The chain is fully rendered in their console. Every signal is there.

Your engineering lead is in your office Thursday morning. They want to push an AI agent to production next Tuesday. It’s a LangChain-based workflow agent, connected through MCP to three internal tools and one external API, with access to a customer database. The framework posters are on the wall. Your team has spent two quarters standing up runtime observability. And sitting in that chair, you still don’t know whether to say yes.

Surprise, surprise, agentic AI is advancing very quickly, and security isn’t quite keeping up. While most attention in recent times has focused on improving model capability, we’ve often been left wondering how to actually make these systems safe enough to trust with real-world tasks and limited interaction. This challenge has become particularly evident with the rise of platforms like OpenClaw, where autonomous agents can execute multi-step actions with minimal human oversight.

Every so often, something comes along that forces you to recalibrate how you think about cyber risk. Not incrementally, but fundamentally. Claude Mythos feels like one of those moments. The cybersecurity industry has spent decades racing attackers to close vulnerabilities faster. Claude Mythos suggests that race may be entering an entirely new phase. One where speed itself becomes the defining risk factor.

Anthropic’s Mythos announcement is not just another cybersecurity headline. It is a signal. AI is transforming software faster than security teams can adapt. The organizations that win won’t be the ones that simply find more flaws. They’ll be the ones that can prove their software can be trusted. A signal that software risk has entered a new era; one where AI can accelerate both the creation of software and the discovery of its weaknesses faster than human teams can respond.

Static application security testing (SAST) tools help developers quickly catch potential vulnerabilities as they code. However, these tools rely on inflexible rules that often generate a high number of false positives, reducing trust in their accuracy and slowing adoption. To help developers access context-aware vulnerability detection, we’ve released an open source AI-native SAST solution. This tool scans code changes incrementally and surfaces security issues in real time.