AI Agent Security: Everything You Need to Know
AI agents are reshaping enterprise operations, powering everything from intelligent chatbots to autonomous manufacturing systems. These agents act semi-autonomously to interpret data, make decisions, and execute tasks across the board. As organizations adopt these digital workers at scale, identifying and securing AI agents becomes a critical priority.
Types of AI Agents
AI agents can be categorized into several types based on their complexity and autonomy. Each has its own ramifications on AIセキュリティ.
Simple Reflex Agents
These are the most basic forms of agent, and typically what is first thought of when considering traditional chatbots. Simple reflex agents take a single input, such as a pre-set customer service issue, and respond directly based on predefined rules.
Model-Based Reflex Agents
These have a similar structure to the simple reflex agent – rather than a simple trigger, however, model-based agents are able to predict the outcomes of their actions, and select the best one. The ‘best’ is chosen according to some preset conditions, such as a self-driving car’s goal to:
- Not only reach a destination
- React to speed limit signs
- Take road traffic behavior into account as it does so
Goal-Based Agents
Rather than input-based responses, more modern agents are able to be driven by outcome.
This results in a far more open-ended model structure, as the AI places an end-goal in the focal point instead of a trigger off of which it reacts. Goal-based agents can include delivery robots, which turn the goal of ‘reach this destination’ into a set of step-by-step navigational actions.
Utility-Based Agents
Unlike basic goal-driven agents that focus solely on achieving a specific outcome, utility-based agents first evaluate how good a possible outcome is.
By applying utility functions, these agents assign values to different possible states, letting them to:
- Weigh trade-offs
- Prioritize competing objectives
- Make nuanced decisions that optimize for the best possible result
This approach is especially powerful in uncertain or partially observable environments, where outcomes can’t be predicted with certainty.
Utility-based agents assess the probability and desirability of different scenarios, selecting actions that maximize expected utility across a spectrum of potential futures. One example is an AI agent that manages an investment portfolio.
Rather than simple buy/sell, it must first evaluate multiple factors like:
- Risk tolerance
- Market volatility
- Long-term goals
Learning Agents
Learning agents are built to iterate over time, usually via feedback from previous responses. They consist of several integrated components – usually a learner, critic, and performance model.
At the core is the performance model, which generates an external behavior in response to a prompt or action – the critic component then evaluates the agent’s response against a predefined standard. If it’s not up to standard, the learning model then implements the necessary change within the performance model.
Learning agents can drive even more improvements by also including a problem generator: this proposes new, untested actions to the critic engine.
By combining feedback from the critic with the exploratory suggestions from the problem generator, the agent can adapt its behavior even faster. But, this approach is resource-intensive and usually applied to single, easily-scored use-cases.
Understanding the Threats & Risks in AI Agent Security
AI agents’ security must counter a large amount of risk: they can bring a vast plethora of new attack surfaces and vulnerabilities to an organization.
Knowing what to look out for and what security tools to deploy can keep this risk manageable.
Data Poisoning
Since AI is so dependent on its underlying training set, the most obvious risk to its agents’ security is the data it’s trained on. If adversaries are able to inject malicious or misleading data into the training set, they can then manipulate the agent’s learning process.
The result is AI models that make incorrect decisions when responding to legitimate employees.
Data poisoning can be difficult to detect, in part thanks to the sheer quantity of data that established AI models require. It’s often only when an AI agent processes it into a bizarre or offensive response that poisoned data is discovered.
One possible example is malicious data samples being injected into a banking system’s training data, resulting in specific demographics being denied loans or other bank services.
Model Inversion & Extraction
Attackers may attempt to reverse-engineer a deployed model by observing outputs, letting them infer proprietary training data or replicate the model entirely. This is how Microsoft alleges Chinese AI firm DeepSeek developed their own LLM tool at such a low cost.
(by monitoring OpenAI’s agent responses and using these to build their own version.)
This process is dubbed ‘distillation’, and pits one model as a student and the larger, more established model as a ‘teacher’. This architecture results in a student model that has a similar level of knowledge, but with an even greater degree of specialization.
While OpenAI is trained on billions of pages of text on the internet, other AI models are trained on more sensitive data – companies’ internal models may take customer information into account, or proprietary code. Model extraction therefore risks an agent divulging company secrets to malicious parties.
Prompt Injection
Since AI agents are the outward-facing components of an AI model, they’re often most at risk of prompt injection attacks. This is different from data poisoning – prompt injection relies solely on the agent processing a user-side prompt, rather than uncovering deeper training-level flaws.
Direct prompt injection sees an attacker craft a prompt that then directly alters the model’s behavior, often to the detriment of the parent organization. This could include:
- The agent generating dangerous outputs
- The agent unveiling internal training data or enterprise information
(making prompt injection one of the worst autonomous agent threats.)
More complex attacks often rely on indirect prompt injection – this exploits an LLM’s ability to accept input from external websites and files, by loading this third-party content with malicious instructions. This is of particular concern when an organization deploys a multimodal AI, which is able to process multiple data types simultaneously.
As a result, an attacker can exploit the interactions between different modalities, such as issuing prompts with benign text alongside an image that holds malicious instructions. These avenues of attack drastically inflate the risk surrounding AI deployments, and are often highly difficult to detect.
Supply Chain Risk
Since AI is a new tool for many organizations, some may be tempted to implement cutting-edge technology before the necessary security infrastructure is in place. The corresponding agents may operate locally, in the cloud, or across decentralized networks – often integrating with:
- API
- User data
- Other software agents
As such, they must be issued with tight consideration for backdoor and supply chain attacks.
Backdoor attacks pose a significant threat to AI and machine learning systems because compromised models often appear to function normally. For instance, an autonomous vehicle using a model with an embedded backdoor could be triggered – under specific conditions – to ignore stop signs.
This could lead to dangerous accidents and compromise the integrity of both operational performance and research outcomes.
Future Trends in AI Agent Security
As organizations continue deploying AI agents at scale, the security continues to evolve. The guiding objectives for tomorrow’s agent security include two key capabilities:
- Agent identification
- Prompt sanitization
Transparent, Zero Trust Agent Architecture
Zero trust architecture has already made great strides in individually verifying each user and the requests they make. This IDおよびアクセス管理 (IAM) approach is applied with relative ease to AI agents. With non-human IAM in place, each agent is expected to share the identity, context, and security posture of every entity it interacts with, including:
- ユーザー
- サービス
- Other agents
When collected en masse, this data lends far more transparency and control to an organization’s plethora of AI agents. Applying behavioral analysis to these data points allows for further security, since sudden spikes in agent activity or specific prompts allow for the immediate identification of malicious users.
Prompt and Agent Sanitization
AI security best practices are vital for long-term agent security. To support this, prompt validation is a vital feature of prompt injection defense.
By thoroughly checking the format, range, and consistency of input data, AI-driven organizations can prevent attackers from injecting malicious content designed to manipulate or invert the model’s behavior. This layer of input sanitization ensures that only clean, expected data reaches the model.
Another essential principle is keeping the scope of tools and capabilities trained on individual users or tenants.
This minimizes the risk of unauthorized access and ensures that agents operate within clearly defined boundaries. Similarly, all model outputs should be treated as untrusted by default. Even if the underlying model is well-tested, its outputs should not be assumed to be safe or accurate without further validation.
(especially in high-stakes or sensitive environments.)
Another vital guideline is to keep secrets, API tokens, and sensitive credentials out of prompts entirely.
Detecting this demands more features than traditional Data Loss and Protection (DLP) tools can provide, since prompt data is unstructured and highly varied.
Secure AI Prompt Data with Check Point GenAI Security
Almost all AI tools use user data to train future models. Without proper oversight, users may unknowingly share sensitive or copyrighted information with generative AI services, creating the risk of data exposure further down the line.
This lack of visibility into how and where confidential data is used can lead to serious security and compliance concerns. It’s why AI Security Posture Management (AISPM) is a major component within Check Point’s 2025 Cyber Security Report.
To combat these threats, Check Point’s GenAIセキュリティ tool applies AI-driven analysis to all prompts and conversations within generative AI tools used across your organization. This automatically classifies the content that agents are exposed to, and allows for policies to be put in place and enforced.
From a security analyst’s perspective, GenAI Security provides clear insight into which AI tools are being used, what they’re being used for, and the potential risks. Ensuring better control, visibility, and protection.
