The API Security Challenge in AI: Preventing Resource Exhaustion and Unauthorized Access

Agentic AI is transforming business. Organizations are increasingly integrating AI agents into core business systems and processes, using them as intermediaries between users and these internal systems. As a result, these organizations are improving efficiency, automating routine tasks, and driving innovation. But these benefits come at a cost. 

AI agents rely on APIs to access data and functionality from underlying systems. Without APIs, AI agents are useless. If not properly secured, attackers can significantly impact performance and corporate resources. In this article, we’ll explore these security challenges and how Wallarm’s approach to API security helps remediate them. 

Lack of Context and Controls in AI Agents

Broad and excessive permissions are among the biggest problems with AI agents. When AI agents are granted broad or excessive permissions, they inherit those permissions across all the API calls they make, which can allow attackers unauthorized access to internal systems. 

If an API lacks granular access controls – such as role-based access control (RBAC) – an AI agent with broad permissions could interact with any function or dataset exposed by the API. For example, an AI customer support bot designed to retrieve order statuses could also grant access to customer financial data. 

Expanded Attack Surface

While they enhance functionality, AI agents also introduce a new point of entry into backend systems. By their nature, they expose APIs to interact with them. Again, if these APIs aren’t properly secured, they become prime targets for attackers.

Attackers can exploit AI agents’ exposed APIs by bombarding them with queries. This can overwhelm the AI agent’s processing capabilities and result in resource exhaustion. The AI agent, in turn, may consume excessive resources from the backend systems to which it’s connected, causing wider system slowdowns or crashes. There’s also a financial risk where AI APIs are being used. By flooding an agent with queries, attackers can consume AI credits or cause cost overruns with metered services.

Moreover, attackers can exploit API vulnerabilities through techniques such as injection, authentication bypass, and misconfiguration exploits to gain unauthorized access to the AI agent itself or the backend systems it connects to. 

How Wallarm Protects AI Agents 

So, now that we know how agentic AI can facilitate resource exhaustion and unauthorized access, we can look into how Wallarm protects it from these threats.  

Combating Resource Exhaustion

Wallarm helps combat resource exhaustion attacks with real-time threat detection that identifies and blocks malicious traffic and API abuse prevention that uses machine learning to detect automated threats. Moreover, Wallarm goes beyond basic rate limiting and employs intelligent threat detection to distinguish between malicious high traffic and malicious overload attempts. This means that we proactively block attack types like Brute Force (targeting authentication endpoints) and Data Bombs (overloading processing with crafted payloads) proactively. 

Wallarm’s advanced API rate limiting can be used to ensure that sensitive APIs aren’t flooded with requests from AI agents, and that AI APIs don’t generate excessive cost overages. 

Addressing Expanding Attack Surfaces

As noted, AI agents significantly expand an organization’s attack surface. Wallarm helps mitigate this problem with continuous API discovery and management that provides: 

• Automatic API discovery and cataloging to ensure comprehensive oversight, including AI endpoints
• Detection of “shadow APIs” that may lack security controls
• Proactive risk assessment to mitigate threats from newly exposed or undocumented APIs

Implementing Session-Level Protection

Attackers using rotating IPs or distributed attacks can easily circumvent IP-based security. Wallarm overcomes this by providing session-level visibility and tracking the attacker’s entire activity. This allows Wallarm to detect and stop attacks, even after account compromise, regardless of IP changes, making it far more difficult and costly for attackers to succeed.

Detecting and Preventing Novel Threats

AI agents face a huge number of emerging threats, including prompt injection, jailbreaking, data poisoning, and AI framework exploits that traditional security solutions fail to address. Wallarm, however, offers the following capabilities to ensure your AI agents are secure: 

• AI-powered threat detection with deep inspection of API requests
• Behavioral analytics to identify anomalies indicative of attacks
• Proactive research into AI-specific vulnerabilities, such as jailbreaking generative AI models

Flexible Deployment

Furthermore, Wallarm offers a flexible deployment approach, meaning our solution integrates seamlessly into existing infrastructures. The adaptability we offer is important for AI agents operating in complex architectures. Wallarm can be deployed as a reverse proxy or integrated with an API gateway, which means it stands in front of AI agents to inspect and secure the APIs they use to interact with internal systems and data.

Wallarm supports multiple deployment options, including: 

• SaaS deployment at the network edge
• Inline proxy integration with API gateways (NGINX, Envoy, Kong, Apigee)
• Cloud-native deployment via Kubernetes Ingress Controllers or sidecar proxies
• Out-of-band monitoring with technologies like eBPF

These diverse options enable organizations to align deployment with their infrastructure, expertise, and performance needs. Integration with API management platforms like Apigee further enhances security, directing traffic to a Wallarm node for in-depth analysis. 

What’s more, we offer a license model based on request volume rather than deployment, which simplifies decision-making and ensures cost predictability and flexibility, especially in dynamic environments where API usage fluctuates. Want to find out more about Wallarm’s approach to protecting agentic AI? Click here.

The post The API Security Challenge in AI: Preventing Resource Exhaustion and Unauthorized Access appeared first on Wallarm.

*** This is a Security Bloggers Network syndicated blog from Wallarm authored by Tim Erlin. Read the original post at: https://lab.wallarm.com/api-security-challenge-ai-resource-exhaustion-unauthorized-access/