Risk
Data Poisoning and Integrity Issues
Description
Intentional or unintentional corruption of data used for training, fine-tuning, or context retrieval (e.g., RAG), which can manipulate model behavior, create backdoors, or degrade performance.
Example
Adversary alters training, fine-tuning, or context data to cause harmful or biased model outputs
Assets Affected
Dataset / RAG
Mitigation
- Implement stringent data validation, sanitization, and integrity checks
- Ensure data quality and provenance
- Secure data pipelines
- Conduct regular audits of training data sources
Standards Mapping
- ISO 42001: A.7.2, A.7.4
- OWASP Top 10 for LLM: LLM04
- NIST AI RMF: MAP 2.3, MEASURE 2.11
- DASF v2: DATASETS 3.1, RAW DATA 1.7