SAIL

/

Build - AI Security Posture Management

/

Data Poisoning and Integrity Issues

3.1

.

Data Poisoning and Integrity Issues

sail
3.1
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