Peeling Back the Layers: Exploring Critical Security Flaws in Chatbot Systems

This week, I had the opportunity to perform a penetration test on an internal application that featured a chatbot. At first glance, this chatbot appeared to be a simple tool for assisting users and facilitating communication between accounts. It allowed interactions through predefined responses and even enabled file uploads under certain circumstances. However, what piqued my curiosity was the potential security weaknesses lurking beneath this seemingly helpful interface.

As the chatbot sat innocuously at the bottom-left corner of the screen, I couldn’t shake the feeling that there was more to it. It was time to pull back the curtain and start exploring.

The chatbot allowed communication without requiring authentication — initially only with the bot, but after a successful login, it opened up the ability to chat with other users. It also enabled file uploads, albeit with specific format restrictions.

Naturally, I began testing the chatbot’s behavior by sending various inputs to see how it handled unexpected or excessive data. From there, my journey into its vulnerabilities began to unfold. The more I explored, the more the surface-level simplicity gave way to deeper, more dangerous issues.

What struck me early on was how the chat seemed to respond to different inputs and formats. What if it wasn’t filtering or sanitizing what I sent? That question guided me to my first discovery.

After submitting different types of inputs into the chat, I noticed something intriguing. The chatbot was not just reflecting my messages as text — it was interpreting them as HTML! When I sent a message containing HTML tags, they weren’t displayed as part of the text, but rather rendered directly on the page. This revealed a critical HTML Injection vulnerability.

By injecting tags like <h1> or <b>, I was able to manipulate the way chat messages appeared. This vulnerability, though seemingly benign, was a warning sign. If HTML could be injected and rendered, what about more dangerous elements? This wasn’t just about changing the look of messages—it laid the groundwork for potential XSS attacks.

With the HTML Injection in mind, I escalated my testing. My next step was to see whether I could execute JavaScript code within the chat. Sure enough, the chatbot was susceptible to Stored Cross-Site Scripting (XSS).

When I injected a simple JavaScript payload, it persisted within the chat history. Each time another user viewed the chat, the script executed silently in their browser. This meant I could inject malicious code that would steal session cookies, hijack accounts, or perform unauthorized actions on behalf of the victim — all while they were none the wiser.

The stored nature of the XSS made this particularly dangerous. The payload would lie dormant, waiting for a user to access the chat history before it sprang into action. This vulnerability turned the chatbot into a ticking time bomb, ready to exploit any unsuspecting user.

After discovering the XSS vulnerability, I decided to push things further by embedding malicious forms and links into the chat. That’s when I uncovered the potential for Cross-Site Request Forgery (CSRF).

By injecting hidden forms or malicious links, I could trick users into performing actions such as changing their account settings or making unauthorized requests — without their knowledge. When users opened the chat and clicked on these hidden elements, they unknowingly submitted requests that could alter their accounts or settings.

CSRF attacks are insidious because they exploit the trust a website has in the user. This vulnerability allowed me to weaponize the chatbot, tricking users into executing actions they had no intention of performing. And with the chat’s persistence, this danger lingered long after the attack was set in motion.

Next, I turned my attention to the chatbot’s file upload feature. While the front-end interface imposed limits on what could be uploaded and how many files could be sent, I suspected the backend might not be as strict. Using a web proxy, I replayed the upload request multiple times. The results were startling.

The chatbot didn’t enforce the same restrictions server-side, leading to an Unrestricted File Upload vulnerability. When I replayed the request, I noticed that multiple files were being stored on the server, even though the interface only allowed a few uploads at a time. By scripting the process, I managed to upload thousands of files in a short span of time.

The server struggled to keep up. The chatbot slowed down visibly, and the files kept accumulating, which could lead to a potential Denial of Service (DoS). This opened up a whole new vector of attack — one where the chatbot could be exploited to exhaust system resources and crash the server.

After testing the file upload functionality, I circled back to the unauthenticated chatbot. I wondered how the application was keeping track of these sessions and whether they were truly isolated. By manipulating the cookies assigned during an unauthenticated session, I made a fascinating discovery: session persistence.

By exporting and importing cookie values between different browsers, I was able to continue a conversation without authentication and from multiple browsers simultaneously. Messages sent from one browser appeared instantly on another. This allowed me to maintain persistent, concurrent sessions with the chatbot — an ability that raised serious concerns about data exfiltration.

This wasn’t just about a chat conversation anymore. The fact that I could communicate across multiple browsers without authentication meant that attackers could remain anonymous, exfiltrate data, and share files without leaving a trace.

The combined power of these vulnerabilities painted a disturbing picture. The chatbot, designed as a simple assistant, had become a tool for data exfiltration and anonymous communication. The file upload functionality, intended to assist users, could now be leveraged to bypass stringent data loss prevention (DLP) policies.

Since the chatbot was whitelisted from DLP rules, attackers could upload sensitive files without triggering alarms. By using the persistent chat session, these files could be accessed across multiple browsers — allowing internal and external users to exchange files and information, completely undetected. The lack of session traceability further complicated any efforts to track down the source of the breach.

Individually, each vulnerability posed a serious risk, but when combined, they amplified the potential for damage exponentially:

When chained together, these vulnerabilities created a perfect storm. The chatbot, which was supposed to improve user experience, had turned into a backdoor for attackers to infiltrate the organization, steal data, and wreak havoc — all while flying under the radar.

This engagement underscored a critical lesson: never underestimate the complexity hidden within seemingly simple features. Chatbots are often seen as benign elements within an application, but as this test revealed, they can be rife with vulnerabilities that, when left unchecked, can lead to devastating consequences.

The importance of proper input validation, session handling, and backend restrictions cannot be overstated. Just because a feature looks simple doesn’t mean it’s safe. Every line of code must be scrutinized, and every feature must be tested for potential weaknesses.

Furthermore, when security controls — such as DLP — are bypassed, it’s essential to assess the broader context of how data can flow through an organization. A seemingly harmless feature like a chatbot can become an avenue for data leakage, bypassing even the most well-intentioned controls.

In conclusion, this penetration test revealed how a seemingly innocent chatbot could be weaponized through a series of vulnerabilities that, when combined, created a powerful attack chain. This engagement highlighted the importance of thorough security testing, especially in areas of the application that may appear trivial.

Chatbots are no longer just tools for improving user interaction — they have the potential to become conduits for exploitation. In this case, curiosity led to the discovery of multiple vulnerabilities, proving that even the most innocuous features can hide dangerous secrets.

Evaluating this chatbot was a fascinating experience, demonstrating the importance of testing even seemingly minor features for security flaws. Chatbots, often seen as harmless tools, can become serious security risks if not properly secured. This engagement underscored the need for organizations to scrutinize every part of their web applications — from login functionalities to chat interfaces — to ensure that no stone is left unturned in the pursuit of security.

Thank you for your attention. I look forward to sharing more insights in future articles!

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