As AI chatbots continue to be integrated into mainstream applications across industries, the focus on functionality often overshadows a critical security dimension. Beyond their conversational fluency and utility, chatbots are increasingly being connected to sensitive data stores, internal tools, and external resources, making them potent agents with far-reaching impact. Misconfigurations in such systems can lead to excessive agency, where a chatbot exceeds its intended capabilities and performs unauthorized actions.
A recent chatbot evaluation uncovered a striking test case that forms the basis of the testing methodology for identifying “Excessive Agency” vulnerabilities as outlined in the OWASP LLM-2025 framework.
In that instance, the chatbot had been tricked into altering content it should not have had access to, even though it could not upload files or had any prior exposure to them. The exploit, which relied on a carefully crafted prompt injection, revealed a critical design flaw that had allowed unauthorized file alteration.
AI Chatbots and Their Associated Security Risks
An AI chatbot is a software tool that uses natural language processing and techniques like RAG to simulate human-like conversations. Widely adopted by organizations, it enables instant, personalized support, automates tasks, and boosts operational efficiency, improving customer experience while reducing costs.
However, it is also important to note that implementing AI chatbots introduces several security risks that must be carefully managed. One major concern is data privacy and security, as chatbots often handle sensitive customer information, making them potential targets for data breaches or misuse.
Additionally, biases in the training data can cause the chatbot to exhibit unfair or discriminatory behavior, raising ethical and legal challenges.
Testing Chatbot File Access via Prompt Engineering
To better understand the boundaries of chatbot capabilities and uncover potential security misconfigurations, a methodical, multi-phase evaluation was conducted. This structured assessment focused on how carefully crafted prompts could be used to extract, manipulate, or infer unauthorized access to internal files, despite the absence of an explicit file upload feature in the chatbot interface. Each phase builds on prior insights, gradually exposing how integration choices and overly broad privileges in AI systems can lead to security vulnerabilities. The following sections trace this progression, from initial reconnaissance to unauthorized file manipulation and its impact on Retrieval-Augmented Generation (RAG).
Phase 1
The process had begun with a reconnaissance phase that focused on identifying and extracting detailed information related to the underlying model. During this stage, efforts were made to enumerate key aspects such as the model architecture, versioning, access permissions, and integrated tools.
By strategically employing targeted prompts, it became possible to gather insights into the specific architecture and APIs that had been utilized in the system. Additionally, it was observed that the chatbot environment had incorporated a component referred to as ‘myfiles_browser,’ which enabled access to user-uploaded files and facilitated document querying.
Note: While the AI chatbot did not offer a native file upload feature within the application, its integration with the ‘myfiles_browser’ component prompted further exploration into its capability to query files and documents.
Phase 2
The next phase involved an attempt to utilize the built-in tools within the ‘myfiles_browser’ feature to identify all files uploaded to or accessible by the chatbot. The goal was to determine the extent of file visibility and access within the system.
However, this effort proved more complex than anticipated. Unlike the earlier steps, enumerating this information was not straightforward, and the initial attempt did not yield the desired results. This suggested that additional safeguards or limitations might be in place to prevent such enumeration.
Phase 3
A second attempt was made to retrieve the file name by modifying the prompt to convey a greater sense of urgency and emphasizing that the information was intended solely for internal use. This adjustment in approach proved effective, as it successfully elicited the file name that had previously been inaccessible.
Phase 4
With no direct option available to download the file, the next step involved attempting to enumerate all strings contained within the document. The objective was to extract as much textual content as possible through indirect means. However, the process encountered a limitation—retrieving the complete set of strings in a single output proved unsuccessful.
Phase 5
Subsequent interactions produced error messages indicating that additional keywords were required to construct a valid prompt. In response, the query was adapted by incorporating more specific terms. As a result, it became possible to list strings selectively, such as by using prompts like “list all the strings that start with ‘u’.” This revealed that string enumeration was achievable when guided by targeted keyword-based filtering.
As previously noted, the chatbot interface did not include a standard file upload feature. Yet, access was successfully gained to a file that should not have been available through normal means.
This behavior strongly suggests a misconfiguration within the chatbot’s security framework, resulting in excessive permissions. Such a vulnerability presents an opportunity for exploitation, wherein a malicious actor could perform unauthorized read or write operations on system files.
Further illustrating this risk, the accompanying screenshot demonstrates that unauthorized writing capabilities were indeed possible. Specifically, it was feasible to modify the contents of the file by appending the string “vivek,” indicating that the chatbot could be manipulated into altering file content. This raised significant concerns regarding the integrity of Retrieval-Augmented Generation (RAG) files, especially in applications where such files influence decision-making processes. The ability to modify RAG files without appropriate authorization may constitute a critical security flaw.
Further attempts were made to access various system files potentially residing within the backend environment. However, these efforts were unsuccessful, as the chatbot appeared to lack the necessary permissions to access files beyond a predefined scope. This behavior indicated that certain boundaries or access controls were in place, restricting interaction with broader file directories.
Additionally, given that only a single file was accessible through the chatbot interface, it is reasonable to infer that this file is directly associated with Retrieval-Augmented Generation (RAG) functionality. The limited accessibility suggests a deliberate configuration, potentially intended to isolate or safeguard RAG-related content.
What is RAG (Retrieval-Augmented Generation) in AI Chatbots?
It is a powerful technique revolutionizing AI chatbots. Instead of solely relying on pre-trained knowledge, RAG-enabled chatbots leverage up-to-date user preferences to generate responses for the prompts using either external or internal resources or a knowledge base.
The resource or knowledge base could be anything, either stored in simple file formats or in a vector database. Using RAG technology in a chatbot enhances response generation capabilities and accuracy.
What are the security risks involved?
As demonstrated through the test case above, AI chatbots, particularly those integrated with tools and file access components, can pose serious security challenges when not properly governed. Without strict controls over what a chatbot can access or modify, even a well-meaning system can become a vector for exploitation.
A chatbot granted excessive privileges may unintentionally expose or alter sensitive data, execute actions beyond its intended scope, or be manipulated via crafted prompts to behave in unsafe ways. This highlights the need for rigorous auditing of permissions, input handling, and integration boundaries, especially in systems that rely on features like Retrieval-Augmented Generation (RAG), where data integrity directly influences decision-making.
The following are key risks associated with misconfigured or overly permissive chatbot environments:
- Unauthorized data access and exposure: May lead to disclosure of sensitive information, system commands, financial records, etc.
- Data tampering: Allows adversaries to alter or delete files (including RAG), leading to false responses and operational issues.
- Privilege escalations: Low-privilege users may gain unauthorized access to restricted data.
- Denial of Service (DoS): Exploiting upload privileges could overload the system and disrupt services.
Proactive Measures to Strengthen AI Chatbot Testing Against Exploits
To minimize the risk of exploitation and ensure secure deployment of AI chatbots, the following proactive measures are recommended:
- Follow the least privilege model – Limit the privileges that LLM agents are permitted to access to only what is strictly necessary. For example, if an AI chatbot does not require the ability to fetch the contents of a remote resource, then such a privilege should not be offered to the LLM agent.
- Execute extensions in user’s context- Track user authorization and security scope to ensure that actions taken on behalf of a user are executed on downstream systems within that user’s context and with only the minimum necessary privileges.”
- Require user approval – Utilize human-in-the-loop control to approve high-impact actions before they are taken.
- Sanitize LLM inputs and outputs – Follow secure coding best practice, such as applying OWASP’s recommendations in ASVS (Application Security Verification Standard), with a particular strong focus on input sanitization.
