OWASP and LLM
Last updated
Last updated
Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized industries, enabling unprecedented advancements in automation, data analysis, and user interaction. From chatbots to autonomous systems, the deployment of Large Language Models (LLMs) is expanding rapidly. However, this growth brings with it a host of security concerns that require proactive measures.
The penetration testing (pentest) of AI/ML systems has emerged as a specialized discipline within cybersecurity. Unlike traditional applications, AI systems, particularly LLMs, involve unique challenges such as understanding model behaviors, safeguarding training datasets, and mitigating risks from adversarial inputs. Pentesting AI systems involves probing for vulnerabilities not only in the code but also in the data, algorithms, and their interactions with external systems.
Key areas of focus in AI/ML pentesting include:
Identifying weaknesses in data pipelines.
Detecting adversarial attacks, such as poisoning and evasion tactics.
Ensuring robustness against unauthorized manipulations of model outputs.
Recognizing these challenges, the OWASP Top 10 for LLM Applications 2025 provides a structured framework to identify and address the most pressing security risks in LLM deployments. This guide bridges the gap between traditional cybersecurity practices and the nuanced requirements of modern AI systems.
Download OWASP Top 10 for LLMs List for 2025 .
Essential guidance for CISOs managing the rollout of Gen AI technology.
