Securily - Cyber Security Compliance

⛔ Attackers are no longer just using AI for reconnaissance.

They’re starting to use LLM agents for post-exploitation: extracting credentials, moving laterally, accessing secrets, and interacting across cloud environments after initial compromise. Recent research observed an LLM agent autonomously performing post-compromise actions following exploitation of a vulnerable system.

This is a meaningful shift.

As agentic AI becomes part of both offense and defense, security teams will need far greater visibility into identities, permissions, secrets, and behavioral activity across infrastructure.

AI is changing the attack lifecycle itself.

👉 https://thehackernews.com/2026/05/attackers-use-llm-agent-for-post.html

Attackers Use LLM Agent for Post-Exploitation After Marimo CVE-2026-39987 Exploit LLM-driven attackers exploited CVE-2026-39987 on May 10, 2026, to steal credentials and exfiltrate a PostgreSQL database.

AI vulnerabilities are not behaving like traditional software bugs.

Pe*******on tests are uncovering higher-severity flaws, larger blast radii, and attack paths that spread across agents, APIs, workflows, and connected systems far faster than legacy applications ever did.

The problem isn’t just more vulnerabilities.

It’s that most organizations still don’t fully understand who owns remediation once AI systems are operating autonomously in production.

AI changes the attack surface. Security testing has to evolve with it.

https://www.csoonline.com/article/4166185/pen-tests-show-ai-security-flaws-far-more-severe-than-legacy-software-bugs.html

Pen tests show AI security flaws far more severe than legacy software bugs Pe*******on tests of AI systems expose significantly higher severe-flaw density when compared to legacy apps. New attack surfaces, larger blast radii, and unclear remediation ownership compound the risks.