Các cách xuất hiện
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.
Nền tảng áp dụng
Ngôn ngữ
Lớp: Not Language-Specific (Undetermined)
Kiến trúc
Lớp: Not Architecture-Specific (Undetermined)
Công nghệ
Tên: AI/ML (Undetermined)
Lớp: Not Technology-Specific (Undetermined)
Hậu quả phổ biến
| Phạm vi |
Tác động |
Khả năng xảy ra |
Integrity Other | Varies by Context, Unexpected State
Ghi chú: The product can generate inaccurate, misleading, or
nonsensical information. | |
| Other | Alter Execution Logic, Unexpected State, Varies by Context
Ghi chú: If outputs are used in critical decision-making
processes, errors could be propagated to other systems or
components. | |
Các biện pháp giảm thiểu tiềm năng
Phases : Implementation // System Configuration // Operation
Develop and adhere to robust parameter tuning
processes that include extensive testing and
validation.
Phases : Implementation // System Configuration // Operation
Implement feedback mechanisms to continuously
assess and adjust model performance.
Phases : Documentation
Provide comprehensive documentation and
guidelines for parameter settings to ensure consistent and
accurate model behavior.
Phương pháp phát hiện
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.
Hiệu quả : Moderate
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.
Hiệu quả : Moderate
Ghi chú ánh xạ lỗ hổng
Lý do : 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.
Bình luận : 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.
Ghi chú
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.
Tài liệu tham khảo
REF-1487
We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs
Joseph Spracklen, Raveen Wijewickrama, A H M Nazmus Sakib, Anindya Maiti, Bimal Viswanath, Murtuza Jadliwala.
https://arxiv.org/abs/2406.10279
Gửi
| Tên |
Tổ chức |
Ngày |
Ngày phát hành |
Version |
| Lily Wong |
MITRE |
2024-06-28 +00:00 |
2025-09-09 +00:00 |
4.18 |
Sửa đổi
| Tên |
Tổ chức |
Ngày |
Bình luận |
| CWE Content Team |
MITRE |
2026-04-30 +00:00 |
updated Relationships |