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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsAI agents inherit many familiar software, identity, and AI security weaknesses—but they can make those weaknesses more consequential by connecting model outputs to tools, credentials, data, and real-world actions. The practical security question is not only whether an agent can be manipulated; it is what it is allowed to do when that happens.
Do AI agents create new security risks?
“AI agents don’t create new risk” works as a provocation, not a literal security conclusion. Agents inherit vulnerabilities found in ordinary software and identity systems, as well as risks tied to AI models, data, and outputs. But connecting a model to software functionality can create distinct challenges: an instruction in content the agent reads may influence what it does next, and its tools or credentials can turn that influence into an action.
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NIST’s Center for AI Standards and Innovation (CAISI) summarized the overlap in its January 12, 2026 request for information about securing AI agent systems: “Some risks overlap with other software systems, such as exploitable authentication or memory management vulnerabilities.” The same announcement also identifies adversarial data, insecure or poisoned models, and harmful actions that can occur without an attacker—for example, specification gaming or misaligned objectives.
So the useful distinction is between familiar weaknesses and changed consequences. A flawed permission system is not unique to agents; an agent that can use that permission autonomously may make the flaw easier to trigger or harder to notice. NIST’s AI security and resilience overview says existing frameworks do not comprehensively cover several AI-related attack surfaces and abuses. Conventional security controls remain necessary, but they should not be assumed to cover every agent-specific interaction.
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What risks do AI agents inherit?
Software and infrastructure vulnerabilities
Agents run on software and infrastructure that can have familiar weaknesses, including exploitable authentication or memory-management flaws. They may also interact with systems whose availability, integrity, or confidentiality can be compromised. Securing the agent’s model does not repair an insecure service or an overly permissive integration it can call.
Identity and access-control weaknesses
An agent’s effective authority comes from its identity, credentials, and the downstream systems that accept its requests. A shared service account with broad access can give a narrowly scoped task more power than it needs. NIST’s NCCoE notes that traditional identity and access management approaches may not fully address emerging challenges when agents take autonomous actions; its Agentic AI Identity and Authorization project hub describes practical guidance work that is still iterative.
AI-specific data and model risks
AI systems also face risks involving training or output data, adversarial inputs, and insecure models, including models subject to data poisoning. Those issues can affect what an agent believes or produces; the agent’s tools then determine whether a bad output remains a bad answer or becomes a harmful operation.
How can prompt injection make an AI agent take actions?
Indirect prompt injection can arrive in data an agent is asked to read rather than in the user’s direct request. A webpage, document, or other content source may contain malicious instructions intended to steer the agent. NIST CAISI calls this agent hijacking. In its technical blog, published January 17, 2025 and updated December 19, 2025, NIST describes the threat this way: “Currently, many AI agents are vulnerable to agent hijacking, a type of indirect prompt injection in which an attacker inserts malicious instructions into data that may be ingested by an AI agent, causing it to take unintended, harmful actions.”
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The potential outcome depends on the agent’s capabilities and permissions. NIST gives examples including remote code execution through a command-line-enabled agent, exfiltration of cloud files, and automated phishing. These are possible attack paths, not abilities every agent has: a document-only agent without those tools or access cannot perform those specific actions.
Filtering suspicious text may help, but it cannot serve as the sole security boundary. The agent may misinterpret content, and a model should not be trusted to decide on its own whether a requested operation is authorized. Limit what tools can do and enforce permission checks in the systems those tools call.
Why an agent’s capabilities matter as much as its model
OWASP’s GenAI Security Project groups a central design problem under “excessive agency.” Its LLM06:2025 guidance states: “The root cause of Excessive Agency is typically one or more of: excessive functionality; excessive permissions; excessive autonomy.” These factors compound: an agent is more dangerous when it has broad tools, broad access, and discretion to act without a person checking consequential decisions.
Excessive functionality
A task may require reading documents, while an available extension also permits editing or deleting them. Unneeded functions increase the number of things a compromised or misdirected agent could do.
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Excessive permissions
A connector may have wider database or file access than the assigned task requires. The risk is not limited to what the agent was asked to do; it includes what its credentials let it do.
Excessive autonomy
An agent may be able to perform a high-impact action without independent approval. Autonomy can make a workflow faster, but it removes a chance for a person or separate control to catch a mistaken or manipulated decision.
What permissions should an AI agent have?
Give an agent the minimum authority needed for its defined task, and make authorization enforceable outside the model. OWASP recommends narrowing extensions and their functions, limiting downstream permissions, using the user’s own authorization context, and requiring human approval for high-impact actions. Its Agentic AI – Threats and Mitigations resource provides a threat-model-based reference for emerging threats and mitigations; OWASP is an open security project, not a regulator or binding standard.
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- Choose only necessary tools. Do not expose an agent to extensions or functions unrelated to its task.
- Make functions narrow. Prefer a limited operation over a general-purpose tool when the task does not require open-ended capability.
- Scope access to the task and user. Avoid broad shared credentials where a user-bound, task-appropriate authorization context is feasible.
- Enforce authorization downstream. The service that reads, changes, sends, or deletes data should check whether the operation is permitted; the model’s judgment is not an access-control policy.
- Put approval gates around high-impact actions. Require explicit human approval where an action could have significant consequences.
- Log, monitor, and rate-limit activity. These measures can help detect abuse and limit damage, but they do not replace preventive access controls.
How do you secure AI agents?
Treat security as a combination of constrained capability, independent authorization, oversight, and repeated evaluation—not as a prompt-filtering feature or a one-time model test.
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- Define the task and its boundaries. Specify what information the agent may access, which operations it may perform, and which actions need a human decision.
- Reduce tools and privileges to the minimum. Remove unneeded functions, narrow permissions, and avoid credentials that grant broader access than the task requires.
- Enforce permissions in the systems being called. Validate authorization at the downstream service, including for actions initiated through an agent.
- Require approval for consequential operations. Design the workflow so a person can review high-impact actions before they take effect.
- Monitor activity and limit its rate. Keep useful logs, watch for unexpected tool use, and use rate limits to reduce the pace or scale of potential damage.
- Test realistic attack paths repeatedly. Include adversarial content the agent might ingest, evaluate different tasks and outcomes, and revisit tests as systems and attacks change.
Testing needs to consider impact, not only whether an attack succeeds in an aggregate score. In its 2025 AgentDojo discussion, NIST CAISI reported that a red-team evaluation’s attack success on a held-out set of Workspace tasks ranged from 11% for the strongest baseline attack to 81% for the strongest newly developed attack. That is a result from that evaluation setup, not a real-world compromise rate.
NIST also reported that, across five example injection tasks in the AgentDojo evaluation, average success rose from 57% after one attempt to 80% after 25 attempts per task. This illustrates how repeated attempts can change measured results; it is not a forecast for every deployed agent. NIST cautions that task success and consequences vary, and probabilistic model outputs can make repeated testing important. Neither figure establishes a population-wide rate of agent compromise.
What security guidance exists—and what remains in progress?
NIST is developing security control overlays for single-agent and multi-agent use cases, drawing on its cybersecurity control and secure-development resources. That work shows established controls are being adapted to agent contexts, not that a single complete framework already covers every deployment.
NIST’s NCCoE identity and authorization project is also iterative. Its resource hub says a concept paper was published in February 2026 and received more than 600 responses. The hub describes work toward a practical SP 1800-series guide; that planned guide should not be treated as a completed standard. For current implementation decisions, teams can use established security practices alongside agent-focused guidance and adapt them to the tools, identities, and consequences in their own systems.
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