Will Agentic AI Raise New Cyber Risks?

Agentic AI—systems that can act with some autonomy, pursue goals, and coordinate multiple tools—brings both powerful capabilities and serious security concerns. This article outlines how such systems can change cyber threats and what organizations should watch for.

What Is Agentic AI?

Traditional AI responds to prompts and stops. Agentic AI can:

• Plan multi-step tasks
• Call tools and APIs
• Interact with software and people
• Adapt behavior based on feedback

In security terms, that means an AI is not just answering questions; it is taking actions that have real consequences inside networks, applications, and supply chains.

New Attack Surfaces and Vulnerabilities

Agentic AI systems need access to tools and data. That creates new attack surfaces:

• Tooling interfaces: APIs, plugins, and connectors used by the AI may be misconfigured or over-privileged.
• Prompt and plan logic: Attackers may manipulate prompts, system messages, or planning modules to steer behavior.
• Model-hosting environments: The infrastructure running these agents (orchestrators, vector databases, model gateways) becomes a target.

An attacker who gains control of the agent’s context or tools might not need root access to a server anymore. Instead, they co-opt the agent to perform harmful tasks “legitimately” through its permissions.

Autonomous Exploitation and Malware

Agentic AI can accelerate offensive operations:

• Automated reconnaissance: Scanning networks, correlating open ports, versions, and misconfigurations without human oversight.
• Exploit chaining: Generating and testing multiple exploit variants, using feedback loops to refine them.
• Adaptive malware: Designing code that adjusts its behavior based on defenses it detects.

Current large models already assist with exploit writing under certain prompts. An agentic system that can run code, iterate, and self-correct could shorten the time between vulnerability disclosure and weaponization.

Social Engineering at Scale

Human-targeted attacks are likely to become more sophisticated:

• Highly personalized phishing: Agents can gather public and internal data to craft messages tailored to roles, projects, or recent events.
• Real-time conversation attacks: Agents could conduct live chats, calls (through voice synthesis services), or support sessions to trick users into sharing credentials or installing malware.
• Multi-channel coordination: Email, chat, support tickets, and social media can be synchronized to increase trust and urgency.

These attacks can run continuously, learn from failures, and improve their scripts and tactics without direct human supervision.

Misuse of Defensive Agentic AI

Organizations will build agentic AI for defense: automated triage, patching, incident response, and configuration management. Misconfiguration or compromise of these defensive agents can make things worse:

• Automated misconfiguration: An agent that “fixes” security issues might open firewall ports, weaken authentication, or disable alerts if goals are not carefully defined.
• Data exfiltration through tools: If the agent can access sensitive logs and documents, prompt injection or takeover can redirect that data externally.
• False sense of safety: Teams may over-trust automated actions and relax manual reviews, amplifying the impact of a single agent failure.

The same autonomy that makes these agents helpful also makes their mistakes more dangerous and far-reaching.

Prompt Injection and Goal Hijacking

Agentic systems are especially vulnerable to prompt injection because they act on information:

• Malicious content in documents or web pages: Internal or external content could contain instructions hidden in text that the agent treats as higher priority than its original policy.
• Goal modification: Cleverly crafted content might persuade the agent to change its objectives, disable checks, or access data it normally should not touch.
• Tool misuse: Once goals or constraints are weakened, the agent may call powerful tools in unsafe ways.

Attacks that once only influenced outputs can now influence actions. Defensive prompt engineering and strict policy enforcement become critical.

Data Privacy and Model Theft

Agentic AI tends to aggregate access:

• Centralized authority: An agent might have broader permissions than any single human user to perform tasks efficiently. Compromising the agent then exposes large swaths of data.
• Shadow data flows: Agents might store context in logs or vector databases, leaking sensitive details if these are not secured.
• Model extraction and fine-tuned secrets: If proprietary models or fine-tuning data contain sensitive information, attackers might attempt model-stealing attacks through automated queries.

Careful scoping of permissions, strong access controls, and auditing of agent activity become vital.

Supply Chain and Third-Party Risks

Agentic AI often depends on:

• External APIs
• Third-party tools
• Model-as-a-service providers
• Open-source components

Compromise of any of these can cascade:

• A tainted plugin could trick the agent into running malicious commands.
• Malicious updates to open-source agents or orchestration frameworks may insert backdoors.
• A third-party model could log or leak prompts and tool outputs.

Supply chain security practices that already matter for software now extend to AI agents and their dependencies.

Guardrails and Governance

Risk is not inevitable. Several controls can lower the chance and impact of misuse:

• Least-privilege design: Give agents narrowly scoped permissions and separate duties across multiple agents where possible.
• Strong guardrails: Policy layers that filter tool calls, outputs, and high-risk actions; human-in-the-loop for sensitive steps.
• Input and content filtering: Detection of prompt injection patterns, malicious URLs, and untrusted content before an agent acts on it.
• Audit and observability: Detailed logs of prompts, plans, tool calls, and changes; anomaly detection to flag unusual agent behavior.
• Rigorous testing and red-teaming: Systematic adversarial testing focused on goal hijacking, social engineering, and data exfiltration.

Organizations treating agentic AI as high-privilege software rather than a “smart assistant” will be better prepared.

Agentic AI will raise cyber risks in both offensive and defensive contexts. The main concern is not that AI suddenly gains independent intent, but that attackers can bend autonomous systems toward their goals or exploit their mistakes at scale. Careful design, strict permissioning, reliable guardrails, and continuous security testing are needed before handing agents meaningful control over critical systems and data.