What are AI fetchers?
AI fetchers are automated systems that retrieve specific pieces of content for the use of artificial intelligence applications. Unlike AI crawlers, which systematically scan large portions of the web, AI fetchers typically access individual URLs or small sets of resources in response to a direct request.
In simple terms, crawlers explore the web broadly, while fetchers go get exactly what an AI needs, when it needs it.
What is the purpose of AI fetchers?
AI fetchers are used to provide AI systems (models) with fresh, targeted information. The need for this information stems from the following activities:
Retrieving a webpage, document, or API response referenced by a user (think of a human googling something and the AI fetcher helping to provide an AI overview on the ask)
Supplying up-to-date content that may not exist in training data
Supporting features like link previews, citations, AI summaries, or fact-checking
Enabling AI tools to interact with external systems or services
How are AI fetchers different from AI crawlers?
The main difference between crawlers and fetchers is in the scope and intent of their activities.
AI crawlers proactively scan and collect content at scale, often for training or indexing
AI fetchers reactively retrieve specific content, usually triggered by a user action or application request
Fetchers are more similar to a browser loading a page or a backend service calling an API than to a traditional web crawler.
What kind of content do AI fetchers retrieve?
AI fetchers typically retrieve:
Individual web pages or articles
Documents such as PDFs or HTML files
API responses and structured data
Media files or metadata required for a specific task
They usually access content one request at a time, rather than scanning entire sites.
What triggers an AI fetcher to access a website?
Common triggers for AI fetching include:
A user pasting or referencing a URL in an AI tool
An AI system needing to verify or summarize a specific source
A request to retrieve real-time data (e.g., pricing, documentation, status pages)
An application workflow that requires external information
In many cases, the fetch would not occur without explicit user or system intent.
How can website owners identify AI fetcher traffic?
Website owners can identify AI fetchers several ways:
By their distinct user-agent strings (essentially an identifying name)
By their request headers indicating automated access
By assessing traffic patterns that resemble API calls rather than browsing
Fetcher traffic is usually lower volume and more sporadic than crawler traffic
Are AI fetchers subject to robots.txt and access controls?
Yes. AI fetchers usually:
Respect authentication requirements, paywalls, and access restrictions
May check robots.txt, depending on implementation
Must comply with website terms of service and legal requirements
Because fetchers retrieve specific content, access controls like login gates are often very effective.
Are AI fetchers a privacy risk?
AI fetchers are generally lower risk than broad crawlers because they:
Access limited, targeted content
Are often tied to explicit user actions
Do not indiscriminately collect data at scale
However, risks can arise if sensitive URLs are fetched unintentionally or if access controls are misconfigured.
What Are the Security Risks of AI Fetchers?
AI fetchers retrieve specific external resources like webpages, documents, or API responses on demand. While they are more targeted than AI crawlers, they still introduce important security considerations if not carefully designed and controlled.
AI fetcher risks
Server-Side Request Forgery (SSRF). Fetchers that accept arbitrary URLs can be abused to access internal services, cloud metadata endpoints, or private networks.
Unauthorized access to sensitive resources. Without strict network and domain controls, fetchers may retrieve internal or restricted content unintentionally.
Credential and token exposure. Fetchers configured with authentication risk leaking cookies, API keys, or privileged credentials through logs, caches, or responses.
Data exfiltration. Attackers can use fetchers as a proxy to extract sensitive data from protected systems and return it externally.
Malicious or adversarial content. Retrieved content may contain exploit payloads or text designed to manipulate downstream AI behavior (called prompt injection).
Abuse and traffic amplification. Open-ended fetching can be exploited to generate excessive traffic, overwhelm services, or mask the origin of requests.
Resource exhaustion. Unbounded fetches may consume bandwidth, compute, or paid API quotas, impacting availability and cost.
Policy and access control bypass. Inconsistent enforcement of robots.txt, authentication, or site policies can create legal and security exposure.
How can you prevent AI fetcher risks?
There are several best practices security teams can implement to help prevent the risks associated with AI fetchers.
URL allowlists and deny lists
Network isolation and egress filtering
Removal of credentials from fetch contexts
Content sanitization and validation
Rate limiting and request quotas
Clear separation between fetched data and model instructions
Bot management and WAF controls
How Fastly can help
Fastly’s Next-Gen WAF offers built-in bot management capabilities to protect your applications from malicious bots while enabling legitimate ones. Prevent bad bots from performing malicious actions against your websites and APIs by identifying and mitigating them before they can negatively impact your bottom line or user experience.
Learn more about the Next-Gen WAF and its bot management capabilities.