Data and model poisoning
Description
Pre-training, fine-tuning, or embedding data is manipulated to alter the model's behavior, compromise integrity, or degrade performance.
Impact
- Alteration of the model's behavior. - Compromise of integrity. - Degradation of performance. - Increased error rates. - Embedding of malicious instructions.
Recommendation
- SSO and MFA to limit who can access your data and AI platform. - Enforce data quality checks on batch and streaming data before they make it to the datasets. - Validate and audit all training datasets. - Implement sandboxing.
Threat
Authenticated attacker from the Internet.
Expected Remediation Time
⏱️ 60 minutes.
Requirements
173 - Discard unsafe inputs262 - Verify third-party components383 - Sandboxing to limit model exposure to unverified data sourcesFixes
Score
Default score using CVSS 4.0. It may change depending on the context of the src.
Base 4.0
Attack vector
N
Attack complexity
H
Attack requirements
N
Privileges required
L
User interaction
N
Confidentiality (VC)
L
Integrity (VI)
L
Availability (VA)
L
Confidentiality (SC)
N
Integrity (SI)
N
Availability (SA)
N
Threat 4.0
Exploit maturity
P
Vector string
CVSS:4.0/AV:N/AC:H/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P