CWE-1434: Insecure Setting of Generative AI/ML Model Inference Parameters
Weakness ID: 1434
Vulnerability Mapping:ALLOWEDThis CWE ID may be used to map to real-world vulnerabilities Abstraction:
BaseBase - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.
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Description
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.
Extended Description
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.
Common Consequences
This table specifies different individual consequences
associated with the weakness. The Scope identifies the application security area that is
violated, while the Impact describes the negative technical impact that arises if an
adversary succeeds in exploiting this weakness. The Likelihood provides information about
how likely the specific consequence is expected to be seen relative to the other
consequences in the list. For example, there may be high likelihood that a weakness will be
exploited to achieve a certain impact, but a low likelihood that it will be exploited to
achieve a different impact.
Impact
Details
Varies by Context; Unexpected State
Scope: Integrity, Other
The product can generate inaccurate, misleading, or
nonsensical information.
Alter Execution Logic; Unexpected State; Varies by Context
Scope: Other
If outputs are used in critical decision-making
processes, errors could be propagated to other systems or
components.
Potential Mitigations
Phase(s)
Mitigation
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.
Relationships
This table shows the weaknesses and high level categories that are related to this
weakness. These relationships are defined as ChildOf, ParentOf, MemberOf and give insight to
similar items that may exist at higher and lower levels of abstraction. In addition,
relationships such as PeerOf and CanAlsoBe are defined to show similar weaknesses that the user
may want to explore.
Relevant to the view "Research Concepts" (View-1000)
Nature
Type
ID
Name
ChildOf
Base - a weakness that is still mostly independent of a resource or technology, but with sufficient details to provide specific methods for detection and prevention. Base level weaknesses typically describe issues in terms of 2 or 3 of the following dimensions: behavior, property, technology, language, and resource.
Class - a weakness that is described in a very abstract fashion, typically independent of any specific language or technology. More specific than a Pillar Weakness, but more general than a Base Weakness. Class level weaknesses typically describe issues in terms of 1 or 2 of the following dimensions: behavior, property, and resource.
Pillar - a weakness that is the most abstract type of weakness and represents a theme for all class/base/variant weaknesses related to it. A Pillar is different from a Category as a Pillar is still technically a type of weakness that describes a mistake, while a Category represents a common characteristic used to group related things.
Class - a weakness that is described in a very abstract fashion, typically independent of any specific language or technology. More specific than a Pillar Weakness, but more general than a Base Weakness. Class level weaknesses typically describe issues in terms of 1 or 2 of the following dimensions: behavior, property, and resource.
The different Modes of Introduction provide information
about how and when this
weakness may be introduced. The Phase identifies a point in the life cycle at which
introduction
may occur, while the Note provides a typical scenario related to introduction during the
given
phase.
Phase
Note
Build and Compilation
During model training, hyperparameters may be set
without adequate validation or understanding of their
impact.
Installation
During deployment, model parameters may be adjusted
to optimize performance without comprehensive
testing.
Patching and Maintenance
Updates or modifications may be made to the model
that alter its behavior without thorough
re-evaluation.
Applicable Platforms
This listing shows possible areas for which the given
weakness could appear. These
may be for specific named Languages, Operating Systems, Architectures, Paradigms,
Technologies,
or a class of such platforms. The platform is listed along with how frequently the given
weakness appears for that instance.
Languages
Class: Not Language-Specific
(Undetermined Prevalence)
Architectures
Class: Not Architecture-Specific
(Undetermined Prevalence)
Technologies
AI/ML
(Undetermined Prevalence)
Class: Not Technology-Specific
(Undetermined Prevalence)
Demonstrative Examples
Example 1
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.
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.
(good code)
Example Language: JSON
{
...
"temperature", 0.2,
...
}
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.
Weakness Ordinalities
Ordinality
Description
Primary
(where the weakness exists independent of other weaknesses)
Detection
Methods
Method
Details
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.
Effectiveness: Moderate
Note:Given the large variety of outcomes,
it can be difficult to design testing to be comprehensive
enough, and there is still a risk of unpredictable
behavior.
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.
Effectiveness: Moderate
Note:Given the large variety of outcomes,
it can be difficult to design testing to be comprehensive
enough, and there is still a risk of unpredictable
behavior.
Memberships
This MemberOf Relationships table shows additional CWE Categories and Views that
reference this weakness as a member. This information is often useful in understanding where a
weakness fits within the context of external information sources.
Nature
Type
ID
Name
MemberOf
Category - a CWE entry that contains a set of other entries that share a common characteristic.
(this CWE ID may be used to map to real-world vulnerabilities)
Reason
Acceptable-Use
Rationale
This CWE entry is at the Base level of abstraction, which is a preferred level of abstraction for mapping to the root causes of vulnerabilities.
Comments
Carefully read both the name and description to ensure that this mapping is an appropriate fit. Do not try to 'force' a mapping to a lower-level Base/Variant simply to comply with this preferred level of abstraction.
Notes
Research Gap
This weakness might be under-reported as of CWE 4.18,
since there are no clear observed examples in
CVE. However, inference parameters may be the root cause
for various vulnerabilities - or important factors - but
the vulnerability reports may concentrate more on the
negative impact (e.g. code execution) or the weaknesses
that the insecure settings contribute to. Alternately,
dynamic techniques might not reveal the root cause if the
researcher does not have access to the underlying source
code and environment.
References
[REF-1487]
Joseph Spracklen, Raveen Wijewickrama, A H M Nazmus Sakib, Anindya Maiti, Bimal Viswanath and Murtuza Jadliwala. "We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs". 2025-03-02.
<https://arxiv.org/abs/2406.10279>.
(URL validated: 2025-09-08)
Content
History
Submissions
Submission Date
Submitter
Organization
2024-06-28
(CWE 4.18, 2025-09-09)
Lily Wong
MITRE
Contributions
Contribution Date
Contributor
Organization
2025-02-28
(CWE 4.18, 2025-09-09)
AI WG "New Entry" subgroup
Participated in regular meetings from February to August 2025 to develop and refine most elements of this entry.
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Description
This table specifies different individual consequences
associated with the weakness. The Scope identifies the application security area that is
violated, while the Impact describes the negative technical impact that arises if an
adversary succeeds in exploiting this weakness. The Likelihood provides information about
how likely the specific consequence is expected to be seen relative to the other
consequences in the list. For example, there may be high likelihood that a weakness will be
exploited to achieve a certain impact, but a low likelihood that it will be exploited to
achieve a different impact.
