Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

At the executive level, vulnerability management stops being a technical exercise and becomes a question of risk ownership, operational tradeoffs, and organizational accountability. When a vulnerability leads to a breach, it has a personal effect on security leaders along with its broader organizational impact. According to Proofpoint’s Voice of the CISO Report, a majority of CISOs claim they are personally blamed ‘always or often’ when a breach occurs, even when defenses were in place.

Security teams are drowning in findings, not because scanners miss things, but because nothing confirms which ones an attacker could actually reach. Seemplicity AI Analysts run the investigation themselves, checking runtime configuration, network reachability, and exploit conditions for each finding, and re-rank your backlog by confirmed exploitability. What rises to the top is backed by evidence. What drops down has been checked and reasoned out.

LiteLLM, a widely deployed open-source AI gateway, is affected by a critical exploit chain that allows unauthenticated attackers to execute arbitrary commands on vulnerable hosts. CISA added CVE-2026-42271 to its Known Exploited Vulnerabilities (KEV) catalog on June 9, 2026, confirming active exploitation in the wild. The Qilin ransomware group has been linked to exploitation activity. What makes this especially dangerous is the chain: CVE-2026-42271 on its own required a valid API key.

As organizations adopt AI agents to build software, security teams face a new challenge: risk is no longer introduced only through the code that gets produced. It emerges continuously through the tools agents use, the actions they take, and the code they generate. This is the problem Evo Agentic Development Security (ADS) was designed to solve. ADS secures all three layers of the agentic development system—what agents use, what they do, and what they generate.

Today, we're announcing Agentic Development Security (ADS), a new Evo solution designed for securing AI-driven software development. AI agents are now active participants in the software development process, selecting tools, executing actions across systems, and generating production-ready code at machine speed.

For years, application security teams have focused on a familiar set of questions: Is the code secure? Are the dependencies vulnerable? Is the build pipeline protected? Are issues being caught before they reach production? Agentic development adds a new question: What systems, tools, instructions, and permissions helped produce this code? AI coding agents are no longer just suggesting snippets or completing lines of code.

Every security team runs vulnerability scans. It’s the follow-up questions that cause headaches: Which of these 12,000 findings matter, who owns the fix, and how do we prove it held? Staring at a massive spreadsheet of identical "Critical" alerts while chasing down overstretched infrastructure teams isn't only tedious, it's a guaranteed path to burnout. That exhausting gap between finding flaws and getting them fixed is exactly where most security programs stall.