The chain of cybersecurity defense includes networks, storage, and other IT/OT/IoT systems, associated software, and the humans who interact with the technology. Humans are often dubbed the weakest link in the cybersecurity chain because of their susceptibility to social engineering combined with fallibility and inconsistent behavior even after training.

But now agentic AI has become the weakest cybersecurity element. Why? Because hackers are rapidly turning AI agents into a powerful new form of “insider threat” that works 24×7 to exfiltrate sensitive data, breach critical systems, or take other disruptive actions.

Why are AI agents so vulnerable to cyber-attack? What do these emerging attack patterns look like, and how can forward-thinking companies address these escalating risks? This article gives business and technical leaders key insights to support agentic AI risk management.

Key takeaways

• Agentic AI is vulnerable to hackers mainly because it is being rushed into use without needed security controls.
• Many AI agents have excessive privileges granting them access to all manner of sensitive data with insufficient identity and access controls.
• Cybercriminals favor a growing array of attack vectors against agentic AI, including prompt injection, supply chain threats, data poisoning, and privilege/identity or credential attacks.
• In effect, AI agents can be “socially engineered” even more easily than humans because of their drive to achieve a result combined with their lack of common sense and suspicion.
• It can be difficult to monitor agentic AI behavior to spot anomalies.
• Unauthorized shadow agents introduce further vulnerabilities and risks.

Why are AI agents so vulnerable to hackers?

As organizations hurry to roll out agentic AI, they often forego needed security controls to protect the associated models, data, tools, APIs, etc. For example, AI agents may suddenly have access to all kinds of sensitive data, often with inadequate access controls. As agents proliferate by the millions, many of them become high-risk identities capable of autonomously performing unauthorized actions like accessing sensitive data, spawning social engineering attacks, generating faulty results, or leaking credentials to cybercriminals.

AI agents are driven by external instructions. These inputs can be simple for hackers to coopt using a wide range of attacks, as outlined below. Hackers are also keen to exploit AI supply chain vulnerabilities found in third-party models and tools used to build AI agents.

From threat modeling and risk management standpoints, AI agents should be viewed not as typical tools or services but as autonomous actors that operate much like human employees—but are even more vulnerable to manipulation.

Many AI agents can read and write data, run applications, execute code, and make independent decisions in pursuit of their goals. As such, they demand comprehensive governance and specialized cybersecurity controls to counter the systemic risks agentic AI poses.

What are some of the most prevalent attacks on AI agents?

As described in the OWASP Top 10 for Agentic Applications 2026 and other sources, AI agents are vulnerable to diverse attacks, many of which resemble conventional cyberattacks. Some of the most prevalent attack scenarios include:

• Prompt injection. Attackers can insert malicious prompts or instructions into the agent’s input stream, directing it to bypass rules, exfiltrate data, or perform other damaging actions (e.g., deleting data, making unauthorized purchases).
• Supply chain attacks. As noted above, AI agents depend on third-party tools and models whose vulnerabilities can compromise an agent much like conventional software.
• “Confused deputy” scenarios. Hackers can direct AI agents with high or excessive privileges (confused deputies) to abuse their access for all sorts of unapproved purposes.
• Data poisoning. Skilled adversaries can corrupt the data used to train an agent’s underlying model, resulting in errors and unpredictable behavior that can be hard to detect without robust governance.
• Identity and authentication attacks. Even compared to traditional identity management for human users, AI agent identity management is complex and prone to risks and gaps that attackers can exploit.

Once compromised, an AI agent can act much faster than a human to plan and perpetrate insider attacks, cause disruption, and avoid detection—all automatically.

Can agentic AI be socially engineered?

Jason Rebholz, CEO and co-founder at Evoke Security, believes that many attacks on agentic AI amount to social engineering because they exploit the AI’s inherent weaknesses.

From this perspective, AI prompt injection is social engineering. A multi-turn attack is social engineering. It’s all about evading any built-in guardrails to trick the AI into performing unexpected, harmful actions. Much like a phishing attack seeks to bypass a human’s prior training. Only potentially much easier because AI is incapable of skepticism or suspicion.

“I always tell people that agents are more susceptible to social engineering than humans,” Jason states. “If we step back and look at the technology, agents don’t have common sense. If I just use Leetspeak to try to bypass some basic AI guardrails, a human is going to catch on to that. But AI agents and the LLMs behind them… They just want to please you. They are going to do anything you ask—and it’s a fundamental weakness in how these systems work.”

Jason adds: “Many organizations are rushing to give AI agents access to tools, data, and other assets you’d never dream of giving to your day one intern. We’re setting ourselves up for a disaster. What’s getting in our way is we haven’t thought through far enough what is going to happen. That is, threat modeling.”

How can you even know if an AI agent is compromised?

How do you monitor your environment for the head-spinning unpredictability of possible AI behavior? Jason Rebholz suggests viewing it like a behavioral analysis approach to combat human insider threats.

“This is more akin to an insider threat,” notes Jason. “So, it is more behavioral analysis. It’s baselining and understanding the intent of the agent. What are its expected objectives?”

Because AI can only approach situations robotically, errors and missteps may go undetected unless caught by human oversight. Such as repeatedly exposing sensitive data and not reporting it, for example.

When agentic AI is granted privilege levels comparable to the human using it, the risks become extreme. AI may have default access to a range of company applications, including email, as well as the needed credentials. Whether due to internal flaws or external attacks, a compromised AI agent can do damage in direct proportion to what systems and data it can connect to.

Of course, you only stand a chance of governing AI agents you know about. “Shadow agents” adopted with no IT oversight introduce new blind spots in your attack surface and even greater risk.

What’s next?

For more guidance on this topic, listen to Episode 156 of The Virtual CISO Podcast with guest Jason Rebholz, CEO and co-founder at Evoke Security.