AI Agent Security Explained: Risks, Threats and Best Practices

AI agent security is the practice of protecting both autonomous AI agents and the systems they interact with. Because agents can plan, access data and take actions across workflows, they introduce risks like prompt injection, tool misuse, memory poisoning and over-privileged access. Securing them requires system-level controls to ensure safe, accountable behavior across the entire workflow.

McKinsey’s 2025 State of AI survey found that 62% of organizations are already experimenting with AI agents and 23% are scaling at least one agentic AI use case. Gartner also forecasts that by 2028, one-third of interactions with generative AI services will use action models and autonomous agents for task completion. As agents move from isolated pilots into workflows that touch critical business systems, security must account for what they’re capable of doing.

AI agent security focuses specifically on AI agents: systems that can interpret a goal, use tools, access data and carry work forward across a workflow. In that sense, it is a subset of general AI security, which covers AI models, data, infrastructure and applications more broadly.

But securing an agent requires more than securing the model. In an agentic system, the model is only one part of the attack surface. The agent may also call tools, query databases, write to memory, pass work to other agents and continue acting after the first response. This is why AI agent security is usually treated as a system-level initiative.

See our guide to enterprise AI security to explore the key layers of risk across an AI system.

A traditional large language model (LLM) interaction is typically linear — prompt in, response out. Even when the output is sensitive or wrong, the failure is usually bounded to that exchange. An AI agent, on the other hand, doesn’t stop at a response, and it touches many different systems. It receives a goal, decides how to pursue it, calls tools, observes the results, adjusts its next step and may keep state for later use.

That architectural difference changes the security model in practical ways. Once an agent can generate SQL, call an API, open a document, write to a memory store or trigger a downstream process, the risk shifts from “bad output” to “unsafe behavior inside a workflow.” Organizations must have confidence that the agent interpreted the goal safely, selected the right action, used the right credentials, stayed within policy and left a trace that a security or platform team can review.

Persistent memory adds another layer. A prompt-response assistant may forget the exchange as soon as the session ends. An agent with short-term and long-term memory can carry state forward across tasks, users or channels. This can improve continuity, but it also creates new persistence risks. A poisoned instruction that lands in memory may continue shaping later decisions even after the original interaction is gone.

Multi-agent coordination expands the challenge. In a system where one agent gathers evidence, another decides which workflow to run and a third executes a task, security has to cover the messages passed between them, the schemas they use, the permissions each one inherits and the conditions under which one agent can influence another. This isn’t just model security. It’s application security, identity security and workflow governance applied to autonomous software behavior.

The reason AI agent security has become urgent is not simply that agents are novel. Organizations are implementing agentic AI quickly, and some are now moving into scaled use. The pace and scope raise the stakes because agents compress time between interpretation and action. An analyst reviewing a suspicious transaction can pause, ask for context and decide not to proceed, while an agent may retrieve a record, call a tool, write a note, send a request and trigger a downstream process in seconds. If the underlying instruction was manipulated, the tool was over-privileged or the memory was contaminated, the error moves through the system.

The business risk is not limited to adversarial attacks, either. Misconfigured access, fragile tool integrations, weak validation and missing audit trails can produce the same operational outcome: an agent accesses data it should not see, takes an action it should not take or produces a result no one can reliably reconstruct later. In practice, that means security teams have to think in terms of workflow integrity. The goal is not merely to keep an attacker away from the model. It’s to make sure the entire sequence of planning, retrieval, action and handoff remains bounded, observable and policy-aware.

Regulatory pressure reinforces this need. The EU AI Act establishes a formal regulatory structure for certain high-risk AI uses, which means agent deployments in regulated or high-impact domains may need stronger controls, documentation and traceability than many early pilots were built to support. Even organizations that are not legally bound to the EU AI Act are increasingly using it as a governance benchmark because it is a practical reference point for documentation, oversight and risk controls in enterprise AI.

See how cybersecurity startup DeepTempo uses deep learning to combat AI-driven security threats:

An AI agent’s attack surface is broader because the agent sits at the junction of interpretation, execution and state. A model-only system can still be attacked, but an agent creates more boundaries where manipulation or failure can occur.

This is where the agent interprets a goal, breaks it into steps and decides what to do next. If an attacker can influence that reasoning path, the agent may pursue an unsafe objective even when each individual tool call looks valid. Prompt injection often starts here, especially when instructions from user input, retrieved documents and external content are processed alongside system instructions without clear trust boundaries or precedence rules.

Tool use is one of the main reasons agents are valuable, and one of the main reasons they are risky. A function call to a CRM, ticketing system, database, payment workflow or code execution environment has security implications. Weak authorization, poor parameter validation or broad execution scope can let an attacker move from manipulated text to concrete action.

Memory can include short-lived conversational context, persistent preferences, retrieved facts, prior decisions or stored summaries that shape future behavior. If writes to memory are not validated, segmented and governed, an attacker may be able to plant instructions or misinformation that remain active across sessions.

Agents often operate with service credentials, delegated permissions or access inherited from a user or application context. If those permissions are too broad, too durable or too loosely separated across tasks, the agent becomes an attractive path for privilege abuse. The principle of least privilege matters here, but so does scoping access by task, by tool and by time.

In multi-agent systems, one agent may ask another to retrieve data, evaluate a condition or perform an action. That creates a message surface. If agent-to-agent messages are not authenticated, schema-validated and constrained by policy, one compromised or malicious agent may influence the rest of the workflow.

The highest-risk failures in agentic systems tend to occur where instructions, tools, identity, memory and communication meet.

Prompt injection is still one of the most discussed AI security risks because it attacks the agent’s control logic directly. A malicious instruction can be inserted by a user, embedded in retrieved content or hidden in a webpage, file or email the agent is asked to inspect. OWASP’s prompt injection guidance treats this as a core LLM risk, and the issue becomes more serious in agentic systems because the compromised instruction can lead to tool use or privileged actions rather than just a distorted answer.

Indirect prompt injection deserves special attention in agentic systems. The attacker does not need direct access to the interface. Instead, they place malicious instructions where the agent is likely to read them during retrieval or browsing. Many enterprise agents are designed to pull context from documents, tickets, knowledge bases and external content as part of normal execution, creating a variety of opportunities for indirect prompt injection.

An agent that can call tools can also be manipulated into calling the wrong tool, calling the right tool with the wrong parameters or chaining actions in a way the designer did not intend. The risk increases when a tool handles sensitive operations such as database queries, workflow approvals, code execution or external API requests. What looks like an ordinary assistant action can become unauthorized access, data modification or process abuse if the control plane around the tool is weak.

Memory poisoning happens when an attacker plants instructions or false context that persist beyond the current exchange. This can cause an agent to behave differently in future sessions, trust the wrong source, favor a malicious endpoint or carry a manipulated assumption into later tasks. For example, indirect prompt injection can poison long-term memory and potentially drive later exfiltration or harmful behavior.

One reason agents are so effective is because they can act on behalf of a user, service or business function. But this mechanism creates risk if permissions are inherited too broadly or retained too long. An agent that has read access to one table, write access to a ticketing system and invocation rights for an external tool has effectively aggregated permissions across three systems — and anyone who compromises the agent inherits all of them.

As multi-agent orchestration becomes more common, communication itself becomes an attack path. One agent may pass an instruction or data object that another agent trusts too readily. A handoff may preserve hidden context the receiving agent was never meant to accept. In practical terms, teams need to treat inter-agent messages more like API traffic: authenticated, schema-validated, logged and constrained by role.

Agents often operate near sensitive context because that is where their value comes from. They read contracts, support records, financial data, logs or internal knowledge. Without clear output controls and access policies, the agent may expose that context through a response, a tool result, a memory write or a handoff to another system. Even when the prompt itself looks harmless, the combination of retrieval plus autonomy can produce over-disclosure.

An agent that retries, replans and loops through tools can also generate runaway behavior. A poisoned plan, malformed dependency or broken instruction chain can trigger repeated calls — escalating cost, latency and operational load. In a multi-agent environment, those failures can propagate as one agent’s bad output becomes another agent’s input.

Understanding the threat landscape is a necessary starting point, but it raises an operational question that threat analysis alone doesn’t answer: when a failure occurs in a live deployment, what do you do?

Agentic systems introduce challenges that traditional incident response playbooks weren’t designed to handle. For example, a compromised agent may have already written to memory, triggered downstream processes or handed state to another agent before the problem was detected.

The first requirement is containment capability. When an agent begins behaving unexpectedly — executing unusual tool call patterns, accessing data outside its normal scope, producing outputs that suggest a compromised instruction chain — teams need a way to stop the workflow without necessarily taking down the entire system. This means building session termination and agent isolation into the architecture rather than treating them as emergency measures. A kill switch that requires manual infrastructure intervention is slower and riskier than one that operates at the orchestration layer.

Rollback is harder for agents than for stateless systems because agents may have already written to memory, triggered downstream processes or passed state to other agents before the problem was detected. Incident response planning should map which agent actions are reversible and which are not, and treat irreversible action types with additional controls upstream. For actions that cannot be undone, the response plan shifts toward containment, notification and impact assessment.

Audit log completeness is critical during an investigation. The goal is to reconstruct the full sequence — which instruction the agent received, how it interpreted the goal, which tools it called, what parameters it used, which data it retrieved, what it wrote to memory and what it passed downstream. Gaps in that record make it difficult to determine the scope of an incident or confirm that containment was effective. Log design should be treated as a security requirement, not an operational convenience.

Finally, agent incidents often involve a question the team hasn’t faced before: was this a manipulated instruction, a misconfigured permission, a model behavior issue or a tool integration failure? Response playbooks should account for that diagnostic uncertainty and include steps for preserving evidence before the session state is lost.

Securing AI agents works best when controls are attached to each stage of the loop rather than concentrated at the model edge.

System instructions should be narrow, task-specific and explicit about tool boundaries, allowed data sources and prohibited actions. Inputs should be normalized and validated before they reach the agent, especially when the system ingests retrieved content, file contents, email text, web content or inter-agent messages.

Every tool call should pass through explicit permission checks. The agent should not be able to invoke a tool simply because it knows the tool exists. Sandboxing is also recommended, especially for code execution, document transformation, browser access and connectors that reach outside a controlled environment. Tool availability should be narrowed by task so that the agent only sees the functions required for the current operation, not the full tool catalog.

Logging tool calls as first-class security events is equally important. When an incident review starts, teams need to know which tool was called, with what parameters, under which identity, against which resource and in response to which part of the workflow.

Memory should be segmented by type and by trust level. Preferences, working notes, retrieved facts and system instructions should not all live in the same persistence layer without policy boundaries. Writes to persistent memory should be validated, and sensitive context should not be stored merely because it was useful once.

Privilege scope should also be short-lived and purpose-bound. Least privilege is a familiar principle, but agentic systems make it more dynamic. The question is not only “What can this agent access?” but also “What can this agent access for this task, using this tool, for this duration?” Short-lived credentials, role-based access control (RBAC) and task-level scoping reduce the chance that an agent’s utility becomes an attacker’s foothold.

If agents communicate, their messages should be authenticated and schema-validated. A receiving agent should know which agent sent the message, what type of message it is allowed to accept and which fields it is allowed to trust. Coordination rules also need policy boundaries. Not every agent should be able to direct every other agent, and influence should not propagate across the system just because a handoff was technically possible.

Component isolation is useful here. If one agent begins to deviate from expected behavior, isolating that component should not require shutting down the full orchestration layer. The goal is fault containment as much as attack containment.

The zero trust framework is ideal for agents because the workflow crosses multiple boundaries. Every request should be authenticated, authorized and evaluated in context, rather than trusted because the agent is “inside” the system. This means continuous verification at each cycle of planning, retrieval, tool use and memory access, not just at session entry.

Technical controls reduce the probability of unsafe agent behavior, but they don't eliminate it. For workflows where the consequences of a wrong action are significant — irreversible data modification, external communications, financial approvals, access provisioning — the most reliable mitigation is to require human review before the action executes.

A useful framing is to classify agent actions by consequence. For example, read-only retrieval carries different risk than a write to a production system, which carries different risk than an outbound message or a triggered workflow. Actions in the higher-consequence categories are good candidates for mandatory approval gates, regardless of how well governed the rest of the pipeline is.

In practice, this means building confirmation steps into the agent’s tool execution flow for designated action types, not relying on the model to recognize when to pause. If approval isn’t received within a defined window, the action should not proceed by default.

AI agent security gets harder when the workflow is spread across too many disconnected layers. Sprawl makes it challenging to control access consistently, inspect what happened after the fact or contain mistakes before they move into downstream systems.

When agents operate inside a platform with built-in governance, role-based permissions and data policies, security controls sit closer to the workflow itself. This does not remove risk, but it does reduce the attack surface compared with architectures that depend heavily on agents calling out to loosely governed external systems. The practical difference is that access can follow existing roles and policies, sensitive data can remain inside the security perimeter, and agent activity can be reviewed in the same environment where the work occurred.

For example, Snowflake Cortex Agents can plan tasks, use tools and work across structured and unstructured data within the Snowflake environment, keeping the workflow close to platform-level controls. When data does not need to move unnecessarily into external tools or sidecar services just to complete a task, the risk of unnecessary exposure and exfiltration is lower.

Observability is just as important. Once an agent is querying data, invoking tools and returning results into a workflow, security teams need audit logs, query history and access tracking that make it possible to trace what the agent saw, what it attempted and how it moved through the task. A governed platform can make that end-to-end visibility much easier to maintain than an architecture where evidence is scattered across multiple services and logs.

The same is true for guardrails. Content filtering, input and output validation, access policies and other platform-level controls are more enforceable when they are applied within the environment the agent already uses, rather than stitched on around the edges after the workflow has been assembled.

The more operating context remains inside one governed environment, the more manageable agent security becomes.

Data is the prize hackers are often seeking in agentic AI attacks. Learn how to secure data in AI workflows.

Organizations securing AI agents usually end up drawing from several frameworks, because the work spans different questions: threat modeling, governance, regulatory exposure and control design.

OWASP’s agentic AI guidance is the most practical tactical reference for many teams because it focuses on concrete threats and mitigations in autonomous systems. It is useful when a security architect or platform engineer needs to think through prompt injection, tool misuse, memory risks, communication boundaries and execution controls in one place.

NIST’s AI Risk Management Framework is designed to help organizations manage AI risk across four functions: Govern, Map, Measure and Manage. It is a good frame for operating model questions: ownership, lifecycle review, evidence collection, control testing and accountability.

The EU AI Act adds the compliance dimension. Not every AI agent will fall into a high-risk category, but agents used in domains covered by the regulation may trigger stronger obligations around documentation, risk management, transparency and oversight. This matters especially when agents influence employment, critical services, regulated decision support or other high-impact processes.

MITRE ATLAS helps teams map adversarial behavior in a more structured way. It serves as a living knowledge base of tactics and techniques for attacks on AI-enabled systems, which can support threat modeling, red teaming and control design. For teams that already use ATT&CK-style thinking in cybersecurity, ATLAS can make AI-specific adversarial reasoning easier to operationalize.

AI agent security is ultimately about controlling how autonomous software interprets intent, accesses data and carries action across systems. This usually means narrowing permissions, validating inputs, constraining tool use, segmenting memory, authenticating every handoff and making the full workflow observable enough to inspect and improve. It also means choosing a platform where governance, data access and traceability are already part of the operating model.

As agent deployments expand, the teams that manage risk most effectively will be the ones that treat agent security as a systems design problem from the start rather than a series of controls bolted on.