[ISM] LLM risk mitigation (OWASP top 10):

[OWASP] Strengthen security of vectors and embeddings (SSS-02-05-08)

Ensure that vectors and embeddings used in LLM-based applications are securely managed, accessed, and validated to prevent unauthorized data access, information leaks, poisoning attacks, and behavioral alterations. Implement robust access controls, data validation mechanisms, and continuous monitoring to mitigate the risks associated with vector-based knowledge retrieval and augmentation techniques.

[OWASP] Implement secure vector and embedding management to prevent exploitation (SSS-02-05-08-01)

Vectors and embeddings play a critical role in retrieval-augmented generation (RAG) systems, enabling LLMs to access external knowledge sources. However, mismanagement of vectors and embeddings can introduce serious vulnerabilities, such as unauthorized data access, embedding inversion attacks, and data poisoning, which can compromise confidentiality, integrity, and trustworthiness of LLM applications. To mitigate these risks, fine-grained access controls must be enforced to prevent unauthorized retrieval of embeddings and to restrict cross-context information leaks in multi-tenant environments. Data validation pipelines should be established to ensure only vetted, trustworthy sources contribute to the knowledge base, preventing manipulation via poisoned data or adversarial inputs. Embedding inversion attacks, where attackers attempt to extract sensitive data from stored embeddings, should be countered with differential privacy techniques and encryption methods to obscure relationships between raw data and vector representations. Logging and monitoring of all retrieval activities should be maintained to detect anomalous patterns, unauthorized access attempts, and unexpected data leakage incidents. In addition, retrieval augmentation should be evaluated for behavioral alterations, as improper tuning can reduce the model’s effectiveness, empathy, or decision-making reliability. Continuous testing and auditing of augmented models should be performed to ensure they retain their intended functionality without introducing biases, conflicting knowledge, or undesirable responses.

Operations

ID	Operation	Description	Phase	Agent
SSS-02-05-08-01-01	Enforce strict access control for vector storage and retrieval	Implement fine-grained permission controls for vector databases to ensure that users and applications can only access the data relevant to their scope. Prevent unauthorized cross-group access in multi-tenant environments.	Development	Security team, AI engineers, DevOps team
SSS-02-05-08-01-02	Implement robust data validation and filtering	Develop automated pipelines to validate, sanitize, and classify input data before embedding into the vector database. Implement filtering mechanisms to detect hidden adversarial content, such as invisible text-based poisoning attacks.	Development	AI governance team, Data engineers, Security team
SSS-02-05-08-01-03	Apply differential privacy and encryption to embeddings	Use differential privacy techniques to prevent attackers from extracting meaningful data from stored embeddings. Encrypt sensitive vector data to mitigate embedding inversion risks.	Deployment	Security team, Infrastructure team
SSS-02-05-08-01-04	Monitor embedding retrieval activities for anomalies	Maintain detailed, immutable logs of all vector retrievals to detect and respond to suspicious queries or unauthorized access attempts. Implement anomaly detection algorithms to flag unusual embedding interactions.	Post-deployment	Security team, Operation team
SSS-02-05-08-01-05	Evaluate the impact of retrieval augmentation on model behavior	Continuously analyze whether retrieval-augmented knowledge affects the model’s performance, empathy, or decision-making consistency. Adjust augmentation parameters to maintain desired response quality while mitigating unintended alterations.	Post-deployment	AI governance team, Data engineers, Development team

References

Industry framework	Academic work	Real-world case
Information Security Manual (ISM-1923) OWASP Top 10 for LLM OWASP Top 10 for LLM (LLM08:2025)