CWE-1434: Insecure Setting of Generative AI/ML Model… | Glexia
CWE-1434 (Insecure Setting of Generative AI/ML Model Inference Parameters) weakness overview with consequences, detection methods, mitigations, related CVEs and…
Glexia's Take · Automated analysis
CWE-1434: Insecure Setting of Generative AI/ML Model Inference Parameters
Insecure Setting of Generative AI/ML Model Inference Parameters represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.
Executive Impact
- Integrity Other: Varies by Context Unexpected State: The product can generate inaccurate, misleading, or nonsensical information.
- Other: Alter Execution Logic Unexpected State Varies by Context: If outputs are used in critical decision-making processes, errors could be propagated to other systems or components.
Developer Pattern
CWE-1434 is the kind of defect developers can usually prevent with explicit validation, safer framework defaults, and tests that exercise hostile input or unsafe state transitions.
Automation confidence
high confidence from CWE-1434, 4.20.
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Official CWE Definition
CWE-1434: Insecure Setting of Generative AI/ML Model Inference Parameters
The product has a component that relies on a generative AI/ML model configured with inference parameters that produce an unacceptably high rate of erroneous or unexpected outputs.
Generative AI/ML models, such as those used for text generation, image synthesis, and other creative tasks, rely on inference parameters that control model behavior, such as temperature, Top P, and Top K. These parameters affect the model's internal decision-making processes, learning rate, and probability distributions. Incorrect settings can lead to unusual behavior such as text "hallucinations," unrealistic images, or failure to converge during training. The impact of such misconfigurations can compromise the integrity of the application. If the results are used in security-critical operations or decisions, then this could violate the intended security policy, i.e., introduce a vulnerability.
Developer And Remediation Guidance
How teams prevent and detect this weakness
Causes
- Assume the product offers an LLM-based AI coding assistant to help users to write code as part of an Integrated Development Environment (IDE). Assume the model has been trained on real-world code, and the model behaves normally under its default settings. Suppose there is a default temperature of 1, with a range of temperature values from 0 (most deterministic) to 2. Consider the following configuration. The problem is that the configuration contains a temperature hyperparameter that is higher than the default. This significantly increases the likelihood that the LLM will suggest a package that did not exist at training time, a behavior sometimes referred to as "package hallucination." Note that other possible behaviors could arise from higher temperature, not just package hallucination. An adversary could anticipate which package names could be generated and create a malicious package. For example, it has been observed that the same LLM might hallucinate the same package regularly. Any code that is generated by the LLM, when run by the user, would download and execute the malicious package. This is similar to typosquatting. The risk could be reduced by lowering the temperature so that it reduces the unpredictable outputs and has a better chance of staying more in line with the training data. If the temperature is set too low, then some of the power of the model will be lost, and it may be less capable of producing solutions for rarely-encountered problems that are not reflected in the training data. However, if the temperature is not set low enough, the risk of hallucinating package names may still be too high. Unfortunately, the "best" temperature cannot be determined a priori, and sufficient empirical testing is needed. In addition to more restrictive temperature settings, consider adding guardrails that test that independently verify any referenced package to ensure that it exists, is not obsolete, and comes from a trusted party. Note that reducing temperature does not entirely eliminate the risk of package hallucination. Even with very low temperatures or other settings, there is still a small chance that a non-existent package name will be generated.
Remediation
- Implementation System Configuration Operation: Develop and adhere to robust parameter tuning processes that include extensive testing and validation.
- Implementation System Configuration Operation: Implement feedback mechanisms to continuously assess and adjust model performance.
- Documentation: Provide comprehensive documentation and guidelines for parameter settings to ensure consistent and accurate model behavior.
Detection
- Automated Dynamic Analysis: Manipulate inference parameters and perform comparative evaluation to assess the impact of selected values. Build a suite of systems using targeted tools that detect problems such as prompt injection (CWE-1427) and other problems. Consider statistically measuring token distribution to see if it is consistent with expected results.
- Manual Dynamic Analysis: Manipulate inference parameters and perform comparative evaluation to assess the impact of selected values. Build a suite of systems using targeted tools that detect problems such as prompt injection (CWE-1427) and other problems. Consider statistically measuring token distribution to see if it is consistent with expected results.
Mappings
Related CVEs, CWEs, and ATT&CK context
Related CWEs
No related CWE relationships are published yet.
ATT&CK Relevance
ATT&CK relevance is shown only when reviewed or responsibly inferred.