Agentic AI Security Threats, Defenses, Evaluation & Open Challenges copertina

Agentic AI Security Threats, Defenses, Evaluation & Open Challenges

Agentic AI Security Threats, Defenses, Evaluation & Open Challenges

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AI Security Certification and Training:

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To address these challenges, the Certified AI Security Professional (CAISP) certification provides the skills needed to secure the AI supply chain and infrastructure. The course covers:

The emergence of Agentic AI represents a fundamental paradigm shift in cybersecurity. Unlike traditional, static software, agentic systems are defined by their autonomy, planning capabilities, and ability to use tools to execute multi-step goals. This shift means defenders are no longer just securing code, but rather dynamic, goal-driven entities that can be turned against their creators.

The Taxonomy of Threats

The attack surface for these agents is vast, with several critical vectors identified in the sources:

Prompt Injection and Jailbreaking: This is the primary method for hijacking an agent. Attackers use direct injection (malicious commands fed directly) or indirect injection (poisoning data the agent processes, such as a webpage or document) to override core instructions.

Autonomous Exploitation: A compromised agent can effectively become an autonomous hacker. It can independently scan for "one-day vulnerabilities" or execute website exploits without further human intervention.

Multi-Agent Mayhem: When agents collaborate using protocols like MCP (Machine Communication Protocol), risks multiply. Attackers can exploit these protocols for impersonation or to coordinate multiple agents into a "digital crime syndicate" to bypass security controls.

Unchecked Autonomy: The speed of AI operation means a minor error can escalate into a major incident before a human can intervene, making minimal oversight a critical vulnerability

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