Defending Against Data Poisoning Attacks on LLMs: A Comprehensive Guide
Blog post from Promptfoo
Data poisoning remains a significant concern in the OWASP Top 10 for 2025, with its scope now covering all stages of the Large Language Model (LLM) lifecycle: pre-training, fine-tuning, and retrieval from external sources. This threat extends beyond training risks to include model poisoning from shared repositories, potentially embedding backdoors or malware in models. The impact of data poisoning includes degraded model performance, biased outputs, and possible legal and financial repercussions for organizations. To mitigate these risks, a comprehensive set of detection and prevention measures is essential, such as data validation, model behavior monitoring, access restrictions, and supply chain security practices. Additionally, tools like Promptfoo aid in red teaming LLM applications to identify vulnerabilities, while case studies of real-world attacks highlight the importance of maintaining data integrity and vigilance in monitoring models' outputs.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 30 | 4,658 | 533 | 180 | +51% |
| RAG | 5 | 2,191 | 260 | 96 | +39% |
| AI Model Fine-tuning | 4 | 1,020 | 186 | 94 | +66% |
| AI Guardrails | 3 | 348 | 91 | 44 | +60% |
| Observability | 3 | 1,252 | 341 | 119 | -31% |
| Vector Search | 2 | 2,885 | 343 | 120 | -34% |
| AI Coding Assistant | 1 | 698 | 90 | 46 | +23% |
| Reinforcement learning | 1 | 202 | 42 | 25 | +330% |
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