We are witnessing the rapid evolution of autonomous AI agents. They have already moved far beyond the role of “smart chatbots”: they work with external content, files, and APIs, interact with payment services, and can operate with one another without human involvement. A distinct digital ecosystem is emerging—one with its own logic, a new attack surface, and a fundamentally different speed at which risks can spread.
Individual incidents such as Moltbook have already demonstrated possible scenarios involving API key leaks, unauthorized data access, and the hijacking of bots. This is not accidental; the risk is systemic in nature.
Language models interpret instructions and data as plain text, without a clear boundary between “what to do” and “what to work with.” That is precisely why prompt injection remains one of the most serious threats. In its LLM Top 10, OWASP ranks prompt injection as the number one risk for LLM-based applications, as it enables direct manipulation of model behavior without exploiting traditional software vulnerabilities.
If an AI agent has access to files and tools or is able to act within external systems, a single injection can quickly escalate from “strange behavior” to unwanted operations and full environment compromise. Even multi-layered defenses degrade when agents are granted excessive privileges and lack clearly defined execution boundaries. Relying on a “quick patch” is therefore unrealistic—what is required are foundational architectural constraints, segmentation, and action validation by default.
The interaction between agents introduces additional complexity. Malicious instructions are transmitted as ordinary messages, tool substitution redirects requests to fake endpoints, and injections propagate through integrated data sources. Under such conditions, isolated failures can easily turn into cascading incidents.
Are we ready? Only partially. For example, the AI Risk Management Framework by NIST offers a high-level foundation for addressing risk. In practice, however, such principles as minimized privilege, trust segmentation, environment isolation, and “human-in-the-loop” controls often turn up only after the first incidents occur.
Risk increases sharply when agents begin exploiting each other’s weaknesses faster than security teams can detect and respond. For that reason, strict privilege limitation, control over sources and tools before execution, continuous behavioral monitoring, and a rethinking of architecture for environments with unpredictable agent interaction must become priorities.
Olga is a recognized expert in IT and information security with 19 years of experience. Among other things, she specializes in information security systems design and implementation. Her profound knowledge of IT technologies and principles of building IT infrastructure put her in the position of the Chairperson of the Committee on IT and Cyber Security of the German-Ukrainian Chamber of Industry and Commerce. Olga is also the CEO of the Ukrainian IT company Silvery LLC.
