Penetration tests of large language models (LLM)

Penetration testing of Large Language Models (LLM) services focuses on identifying and analysing security vulnerabilities LLMs and their integrations. Just as web applications can have weaknesses stemming from improper configurations or inherent flaws in their technology, LLMs also face a unique set of risks. These include adversarial inputs, model manipulation, data extraction attacks, and unintended model behaviour.

Our comprehensive penetration tests aim to uncover these vulnerabilities, ensuring your LLM is resilient against attacks while preserving its functionality. The penetration test is done in accordance with OWASP Top 10 methodology, content of the AI/ML certification by SecOps Group and our own experience.

Adversarial Input
We assess how well the LLM handles adversarial inputs designed to manipulate or confuse the model into providing incorrect or harmful outputs.

Data Extraction
One key vulnerability is whether sensitive information from the training data can be extracted. We simulate attacks aimed at retrieving personal or confidential information that may have been unintentionally embedded in the model.

Insecure Output Handling
We assess how LLM outputs are handled within the larger system architecture. This includes testing for potential misuses like Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), Server-Side Request Forgery (SSRF), privilege escalation, and remote code execution triggered by unsanitized LLM responses.

Prompt Injection Attacks
We test for susceptibility to prompt injection, where attackers manipulate prompts to bypass safeguards and retrieve unintended outputs, such as confidential or restricted information.

Model Misuse Scenarios
We explore scenarios where the model could be used in ways that deviate from its intended purpose, potentially enabling fraudulent activity or harmful uses.

Model Behaviour and Bias
This part involves probing for biases in responses and testing for inappropriate outputs in sensitive contexts

Authentication and Authorization in Integrated Systems
For models integrated into larger applications, we test the strength of authentication mechanisms and whether an attacker can escalate privileges or bypass restrictions.

Model Denial of Service
We explore the possibility of using resource-intensive operations and inputs that can degrade the performance or availability of the LLM. The vulnerability is magnified due to the resource-intensive nature of LLMs and unpredictability of user inputs.

Training Data Poisoning
By simulating attacks where training data is tampered with, we test for vulnerabilities that could introduce biases, security gaps, or ethical concerns. Sources include Common Crawl, WebText, OpenWebText, & books.