Sweet Attack: Using Agentic AI for Continuous Runtime Red Teaming

The cybersecurity industry is witnessing a transition toward more autonomous defensive and offensive capabilities. Sweet Security recently announced the launch of Sweet Attack, a platform designed to address the limitations of traditional vulnerability management through the implementation of agentic AI and runtime visibility. According to SecurityWeek, this platform aims to counter what industry experts call the ‘Mythos Moment’—the point at which AI agents begin to autonomously perform complex, multi-step tasks in production environments.

The Shift to Agentic AI in Security Operations

Traditional red teaming and Breach and Attack Simulation (BAS) often rely on pre-defined scripts or manual intervention to test defenses. These methods frequently lack the context of a live environment, leading to a surplus of alerts that do not reflect actual risk. The Sweet Security runtime intelligence platform seeks to solve this by observing how an application behaves in real-time. By understanding which processes are running and which network connections are active, the system can differentiate between a theoretical CVE and an exploitable vulnerability that an attacker could use for Lateral Movement.

Agentic AI differs from standard generative AI because it does not merely suggest code or text; it possesses the agency to execute tasks, evaluate the outcome, and adjust its strategy. In a security context, this means an agent can simulate a sophisticated TTP by pivoting through a network much like a human adversary would, identifying weaknesses in the security posture that static scanners miss.

How to use agentic AI for red teaming

To effectively implement this technology, organizations must move away from the ‘scan-and-patch’ mentality. Using agentic AI for red teaming involves deploying agents that can perform autonomous attack path analysis across cloud and containerized environments. These agents use runtime data to determine if a specific RCE vulnerability is reachable from the public internet or if a misconfigured identity permission allows for Privilege Escalation.

By simulating these threats continuously, a SOC can validate whether their SIEM or other detection tools are correctly configured to fire alerts during a real breach. This methodology aligns with the MITRE ATT&CK framework by testing the actual effectiveness of controls against known adversary behaviors in a live, rather than lab, setting.

Strategic Implications for Modern Defense

The introduction of Sweet Attack highlights a broader industry move toward Zero Trust architectures that are verified by continuous testing. The ‘Mythos Moment’ represents a challenge for defenders because if attackers can use AI agents to find vulnerabilities, defenders must use equally capable technology to find them first. This necessitates a runtime-centric approach where the security team has deep visibility into the execution layer of their infrastructure.

Focusing on exploitable attack chains rather than raw vulnerability counts allows security teams to prioritize high-risk issues. This reduction in noise is essential for maintaining operational efficiency and ensuring that critical remediation efforts are not buried under a mountain of low-priority alerts. As AI agents become more prevalent, the ability to perform continuous, autonomous testing will likely become a standard requirement for maintaining a resilient security posture.