The Rise of Agentic AI Risk
AI native applications are no longer just generating text or images. They are retrieving data, making decisions, and taking autonomous actions through tool calls and API chaining. This shift creates a new category of risk that traditional application security (AppSec) practices were not designed to address. Security teams now face threats not only from software vulnerabilities but from what these autonomous systems can be made to do by adversaries.
Recent incidents underscore the urgency. A critical flaw was discovered in OpenAI’s Codex coding agent that allowed attackers to steal GitHub authentication tokens (now patched). In another major event, the popular JavaScript library Axios was backdoored in a supply chain attack, distributing a cross platform remote access Trojan. Elastic Security Labs quickly spotted this unfolding attack using a lightweight, AI driven tool designed to assess malicious repository changes.
Impact and Scope Across the Ecosystem
The threat landscape is expanding beyond code flaws. North Korean hacking group ScarCruft has been spying on a Korean ethnic enclave in China by infiltrating Android apps on a regional gaming platform. Meanwhile, bot traffic, much of it AI driven, is reshaping security, infrastructure, and business operations worldwide. As AI models compress exploit timelines to minutes, defenders must shift toward machine speed defense and real time enforcement.
Organizations are responding with new frameworks. The OWASP Agentic AI Top 10 helps translate emerging risks into practical guidance. Startups like Qodo are raising significant funding (a $70M Series B) to address governance and quality in AI generated code. Experts emphasize that as software development moves from human led to agent led, governing AI coding tool adoption is critical before risks become unmanageable.
Source: Healthcareinfosecurity
