AI-powered chatbots are becoming increasingly common in web applications, improving user experience but also introducing new security risks that can be exploited by malicious actors.
In this regard, Synack has identified several vulnerabilities related to AI-based chatbots in web applications.
Vulnerabilities of AI chatbots
3 Major Security Vulnerabilities of AI-Based Chatbots:
- Injection attacks and AI chatbots: Injection attacks are a constant threat, as Synack’s annual report shows. AI chatbots are also susceptible to these attacks. Penetration testing analyzes how chatbots handle inputs, identifying potential vulnerabilities that bad actors could exploit by injecting malicious commands and messages. By identifying these vulnerabilities, organizations can strengthen their defenses and ensure the integrity of their AI chatbots.
Injection attacks, insecure data storage, and improper authorization are key vulnerabilities that can be identified through penetration testing.
- Insecure data storage and privacy risks: AI chatbots often handle sensitive information, so securing data storage is crucial. Pentesting techniques assess storage mechanisms to ensure that user data is protected from unauthorized access, thereby reducing privacy risks and complying with OWASP secure data management guidelines.
- Inadequate authorization and access controls: According to the OWASP Top 10, inadequate authorization and access controls pose a significant risk. AI chatbot pentesting reviews user access levels, ensuring that only authorized individuals can interact with and modify the chatbot’s functionalities, preventing unauthorized access and potential misuse of sensitive data. Additionally, it is essential to perform penetration testing on the surrounding web application and plugins where the chatbot is deployed, as these may be protected by captchas or authentication mechanisms that could be bypassed.
Testing AI chatbots in web applications
As organizations adopt AI chatbots, it is critical to address the security issues identified in the OWASP AI/LLM Top 10 list. Pentesting is presented as a key strategy to systematically assess and harden AI chatbot capabilities in web applications. By conducting tests aligned with OWASP guidelines, organizations can improve their cybersecurity and demonstrate a proactive commitment to managing emerging threats in AI-based technologies.
