CTEM

Sentra Unveils Autonomous AI Hacker for Continuous Attack Path Discovery in Business Environments

Sentra has launched an autonomous AI-driven solution designed to continuously assess organizational security from an attacker’s perspective. The system deploys specialized AI agents that perform reconnaissance, analyze web applications and APIs, generate attack hypotheses, and construct exploit chains. Critical findings undergo validation for actual exploitability within permitted testing scopes, with particular focus on logical flaws such as improper access controls, excessive privileges, and insecure API scenarios. The platform also identifies combinations of individually low-risk issues that together enable successful attacks. Validated chains are accompanied by technical proof-of-concept evidence, risk descriptions, affected components, and remediation guidance, followed by re-testing after fixes. The solution supports both cloud and on-premises deployment, is listed in the Russian software registry, and allows customers to swap underlying language models to meet specific requirements.

Vibe Hacking Rises: Generative AI Lowers Barriers for Offensive Security Operations

Generative AI is reshaping the cybersecurity threat landscape by dramatically reducing the cost of offensive security knowledge. Previously, attackers needed years of experience in reverse engineering and exploit development to conduct meaningful intrusions, but AI now assists with research, code generation, debugging, and adapting known techniques to new environments. This enables less experienced individuals to build functional attack chains in weeks rather than years. The article introduces the concept of vibe hacking, where attackers collaborate with AI assistants using natural language prompts in a process similar to vibe coding. Defenders can no longer rely on the scarcity of skilled adversaries and must shift toward continuous validation frameworks such as CTEM, AEV, and PTaaS. Senior human experts remain essential for risk judgment and business context, even as automation accelerates routine tasks.

Dark Patterns in Vulnerability Management: How Metrics Undermine Real Security

Vulnerability management programs often fail not due to lack of scanners but because of poorly chosen metrics that prioritize reporting over actual risk reduction. Teams focus on closing easy vulnerabilities, meeting CVSS-based deadlines, and improving dashboard numbers while attackers exploit the shortest path to critical assets. The article examines five common traps including total vulnerability counts, context-free SLAs, closure rate targets, static dashboards, and claims of no critical findings. It argues that these metrics create a false sense of security and distort team behavior according to Goodhart's Law. Instead, organizations should adopt attack path metrics, exposure management approaches such as CTEM, and measurements that track real reduction in attacker reachability. The piece highlights MaxPatrol Carbon as an example of tools that model attacker paths rather than isolated CVEs.