1873
Scope Impact Likelihood
The optimizations alter the order of execution resulting
in side effects that were not intended by the original
developer.
Observed Examples
Reference Description
CVE-2017-5715 Intel, ARM, and AMD processor optimizations related to speculative execution
and branch prediction cause access control checks to be bypassed when
placing data into the cache. Often known as "Spectre".
https://www.cve.org/CVERecord?id=CVE-2017-5715
CVE-2008-1685 C compiler optimization, as allowed by specifications, removes code that is
used to perform checks to detect integer overflows.
https://www.cve.org/CVERecord?id=CVE-2008-1685
MemberOf Relationships
This MemberOf relationships table shows additional CWE Catgeories 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 Page
MemberOf 1398 Comprehensive Categorization: Component Interaction 1400 2524
CWE-1039: Automated Recognition Mechanism with Inadequate Detection or
Handling of Adversarial Input Perturbations
Weakness ID : 1039
Structure : Simple
Abstraction : Class
Description
The product uses an automated mechanism such as machine learning to recognize complex data
inputs (e.g. image or audio) as a particular concept or category, but it does not properly detect
or handle inputs that have been modified or constructed in a way that causes the mechanism to
detect a different, incorrect concept.
Extended Description
When techniques such as machine learning are used to automatically classify input streams, and
those classifications are used for security-critical decisions, then any mistake in classification can
introduce a vulnerability that allows attackers to cause the product to make the wrong security
decision. If the automated mechanism is not developed or "trained" with enough input data, then
attackers may be able to craft malicious input that intentionally triggers the incorrect classification.
Targeted technologies include, but are not necessarily limited to:
• automated speech recognition
• automated image recognition
For example, an attacker might modify road signs or road surface markings to trick autonomous
vehicles into misreading the sign/marking and performing a dangerous action.
Relationships
The table(s) below shows the weaknesses and high level categories that are related to this
weakness. These relationships are defined as ChildOf, ParentOf, MemberOr and give insight to
CWE Version 4.14
CWE-1039: Automated Recognition Mechanism with Inadequate Detection or Handling of Adversarial
Input Perturbations
CWE-1039: Automated Recognition Mechanism with Inadequate
Detection or Handling of Adversarial Input Perturbations 1874
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 may want to be explored.
Relevant to the view "Research Concepts" (CWE-1000)
Nature Type ID Name Page
ChildOf 697 Incorrect Comparison 1530
ChildOf 693 Protection Mechanism Failure 1520
Weakness Ordinalities
Primary : This weakness does not depend on other weaknesses and is the result of choices
made during optimization.
Applicable Platforms
Language : Not Language-Specific (Prevalence = Undetermined)
Common Consequences
Scope Impact Likelihood
Integrity Bypass Protection Mechanism
When the automated recognition is used in a protection
mechanism, an attacker may be able to craft inputs that
are misinterpreted in a way that grants excess privileges.
MemberOf Relationships
This MemberOf relationships table shows additional CWE Catgeories 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 Page
MemberOf 1413 Comprehensive Categorization: Protection Mechanism
Failure
1400 2542
Notes
Relationship
Further investigation is needed to determine if better relationships exist or if additional
organizational entries need to be created. For example, this issue might be better related to
"recognition of input as an incorrect type," which might place it as a sibling of CWE-704 (incorrect
type conversion).
References
[REF-16]Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian
Goodfellow and Rob Fergus. "Intriguing properties of neural networks". 2014 February 9. < https://
arxiv.org/abs/1312.6199 >.
[REF-17]OpenAI. "Attacking Machine Learning with Adversarial Examples". 2017 February 4. <
https://openai.com/research/attacking-machine-learning-with-adversarial-examples >.2023-04-07.
[REF-15]James Vincent. "Magic AI: These are the Optical Illusions that Trick, Fool, and Flummox
Computers". 2017 April 2. The Verge. < https://www.theverge.com/2017/4/12/15271874/aiadversarial-
images-fooling-attacks-artificial-intelligence >.
[REF-13]Xuejing Yuan, Yuxuan Chen, Yue Zhao, Yunhui Long, Xiaokang Liu, Kai Chen, Shengzhi
Zhang, Heqing Huang, Xiaofeng Wang and Carl A. Gunter. "CommanderSong: A Systematic
Approach for Practical Adversarial Voice Recognition". 2018 January 4. < https://arxiv.org/
pdf/1801.08535.pdf >.
[REF-14]Nicholas Carlini and David Wagner. "Audio Adversarial Examples: Targeted Attacks on
Speech-to-Text". 2018 January 5. < https://arxiv.org/abs/1801.01944 >.
CWE Version 4.14
CWE-1041: Use of Redundant Code
CWE-1041: Use of Redundant Code