The potential consequences of failing to evolve security approaches are severe and multifaceted — inaction is not an option.
Risks now extend beyond traditional data breaches to the manipulation of autonomous systems that can interact with the physical world. An agent operating with broad permissions can be hijacked through subtle prompt manipulation, turning a helpful assistant into a malicious actor capable of exfiltrating data, executing unauthorized financial transactions or causing physical disruption.
Multiagent systems are also susceptible to chain reactions. A single compromised agent can misdirect other agents, leading to a domino effect of systemic failure, misinformation and unpredictable behavior. Compromised agents can enable malicious goals to rapidly spread across interconnected systems, breaching containment boundaries and amplifying harm.
Data poisoning and model theft present additional risks. Attackers may corrupt an agent’s training data to introduce biases or hidden vulnerabilities. Sophisticated adversaries can also reverse-engineer proprietary models through repeated queries, compromising intellectual property.
The autonomous nature of AI agents also makes traditional compliance frameworks insufficient. Without proper enterprise controls, agentic AI systems that process sensitive data may expose organizations to compliance and regulatory lapses. Violating regulations like the General Data Protection Regulation (GDPR) can result in substantial fines, loss of certifications and reputational damage.
The Open Web Application Security Project (OWASP) Top 10, a list of the most critical security risks for large language models — which serve as the reasoning engine of agentic AI underscores many of these emerging threats, including prompt injection, training data poisoning, and excessive agency. Given these risks, leaders face an urgent imperative to adopt a new security blueprint.