LLM Application Security Penetration Testing

An organization deployed an LLM-based application using Azure and Retrieval-Augmented Generation (RAG) to provide document-based insights with source citations. A security review identified that Azure SAS URLs were exposed in citations, allowing potential unauthorized access to stored documents and complete read/write/delete access to the storage. This vulnerability risked unintended data disclosure beyond user permissions. The issue was mitigated by securing storage access and removing direct SAS URL exposure. The revised approach ensured safe, controlled access to document insights without compromising data security.

An organization implemented an AI-powered application using third-party LLM APIs to process user queries and generate intelligent responses. Blueinfy identified a prompt injection vulnerability caused by insecure handling of user input within the LLM request construction. The issue allowed attackers to manipulate system prompts and influence the model’s behavior. Blueinfy worked with the development team to implement secure input validation, sanitization, and safe prompt design patterns. This remediation ensured controlled LLM behavior and protected the application from prompt injection attacks before production use.

Consider a hypothetical smart home AI agent, "Smart-Home Assistant," designed to control various devices—lights, thermostats, security systems—and provide real-time information about the environment, like weather and traffic. The agent accepts voice commands through a mobile application. What can go wrong? If the system does not properly sanitize user inputs, harmful commands can be executed without verifying their legitimacy against a predefined list. Moreover, if there is no logging, it would become impossible to trace the execution of the attack. We also talk about the strategies for securing AI agents and the measures that can be taken for the same.

An AI application used by a legal organization allowed users to upload personal and shared documents to Azure Blob Storage and query them via a GPT-based chat interface. During a security assessment, Blueinfy identified two major vulnerabilities: lack of authorization controls in storage access that let users view others’ documents, and the risk of indirect prompt injection from malicious uploaded content. The storage misconfiguration enabled unauthorized data retrieval across user-uploaded files, while malicious documents could feed harmful instructions into the LLM. These flaws risked data confidentiality, compliance violations, and potential phishing or exfiltration attacks. Blueinfy helped implement strict permission enforcement at the blob storage layer and introduced guardrails to prevent harmful outputs, ensuring secure document access and safer LLM responses.

An organization implemented a customizable GPT interface that allowed end users to define system instructions and share tailored personas for contextual conversations with an LLM. During a security review, Blueinfy identified that allowing users to modify the system context introduced significant vulnerabilities, as malicious instructions could alter the model’s behavior. Exploits included attempts to exfiltrate chat history, prompt users for sensitive PII, spread misinformation, and present phishing links. These risks not only threatened data security but also posed reputational harm if the application generated biased, abusive, or unsafe outputs. Blueinfy recommended implementing real-time content filtering, moderation tools, and guardrails to block harmful inputs and protect the LLM’s behavior. The improved safeguards ensured safer, controlled interactions with the customized GPT system.