CWE-1351: Improper Handling of Hardware Behavior in Exceptionally Cold Environments
Weakness ID: 1351
Abstraction: Base Structure: Simple
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Description
A hardware device, or the firmware running on it, is missing or has incorrect protection features to maintain goals of security primitives when the device is cooled below standard operating temperatures.
Extended Description
The hardware designer may improperly anticipate hardware behavior when exposed to exceptionally cold conditions. As a result they may introduce a weakness by not accounting for the modified behavior of critical components when in extreme environments.
An example of a change in behavior is that power loss won't clear/reset any volatile state when cooled below standard operating temperatures. This may result in a weakness when the starting state of the volatile memory is being relied upon for a security decision. For example, a Physical Unclonable Function (PUF) may be supplied as a security primitive to improve confidentiality, authenticity, and integrity guarantees. However, when the PUF is paired with DRAM, SRAM, or another temperature sensitive entropy source, the system designer may introduce weakness by failing to account for the chosen entropy source's behavior at exceptionally low temperatures. In the case of DRAM and SRAM, when power is cycled at low temperatures, the device will not contain the bitwise biasing caused by inconsistencies in manufacturing and will instead contain the data from previous boot. Should the PUF primitive be used in a cryptographic construction which does not account for full adversary control of PUF seed data, weakness would arise.
This weakness does not cover "Cold Boot Attacks" wherein RAM or other external storage is super cooled and read externally by an attacker.
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" (CWE-1000)
Nature
Type
ID
Name
ChildOf
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.
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 "Hardware Design" (CWE-1194)
Nature
Type
ID
Name
MemberOf
Category - a CWE entry that contains a set of other entries that share a common characteristic.
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
Architecture and Design
Implementation
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)
Operating Systems
Class: Not OS-Specific (Undetermined Prevalence)
Architectures
Class: Embedded (Undetermined Prevalence)
Class: Microcomputer (Undetermined Prevalence)
Technologies
Class: System on Chip (Undetermined Prevalence)
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.
Scope
Impact
Likelihood
Integrity Authentication
Technical Impact: Varies by Context; Unexpected State
Consequences of this weakness are highly contextual.
Low
Potential Mitigations
Phase: Architecture and Design
The system should account for security primitive behavior when cooled outside standard temperatures.
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 could 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.
[REF-1182] Yuan Cao, Yunyi Guo, Benyu Liu, Wei Ge, Min Zhu
and Chip-Hong Chang. "A Fully Digital Physical Unclonable Function Based Temperature Sensor for Secure Remote Sensing". 2018-10-11.
<https://ieeexplore.ieee.org/abstract/document/8487347/>.
[REF-1183] Urbi Chatterjee, Soumi Chatterjee, Debdeep Mukhopadhyay
and Rajat Subhra Chakraborty. "Machine Learning Assisted PUF Calibration for Trustworthy Proof of Sensor Data in IoT". 2020-06.
<https://dl.acm.org/doi/abs/10.1145/3393628>. URL validated: 2023-04-07.
Content History
Submissions
Submission Date
Submitter
Organization
2020-10-23 (CWE 4.5, 2021-07-20)
Paul A. Wortman
Wells Fargo
Modifications
Modification Date
Modifier
Organization
2022-04-28
CWE Content Team
MITRE
updated Relationships
2022-06-28
CWE Content Team
MITRE
updated Relationships
2022-10-13
CWE Content Team
MITRE
updated References, Related_Attack_Patterns
2023-01-31
CWE Content Team
MITRE
updated Related_Attack_Patterns
2023-04-27
CWE Content Team
MITRE
updated Relationships
2023-06-29
CWE Content Team
MITRE
updated Mapping_Notes
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