CWE-502

Deserialization of Untrusted Data

The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.

CVE-2024-37052 (GCVE-0-2024-37052)

Vulnerability from cvelistv5 – Published: 2024-06-04 11:59 – Updated: 2024-08-02 03:43
VLAI
Summary
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
Impacted products
Vendor Product Version
MLflow MLflow Affected: 1.1.0 , ≤ * (semver)
Create a notification for this product.
lfprojects mlflow Affected: 1.1.0
    cpe:2.3:a:lfprojects:mlflow:1.1.0:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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CVE-2024-37053 (GCVE-0-2024-37053)

Vulnerability from cvelistv5 – Published: 2024-06-04 12:00 – Updated: 2024-08-02 03:43
VLAI
Summary
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
Impacted products
Vendor Product Version
MLflow MLflow Affected: 1.1.0 , ≤ * (semver)
Create a notification for this product.
lfprojects mlflow Affected: 1.1.0 , ≤ * (custom)
    cpe:2.3:a:lfprojects:mlflow:1.1.0:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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CVE-2024-37054 (GCVE-0-2024-37054)

Vulnerability from cvelistv5 – Published: 2024-06-04 12:00 – Updated: 2024-08-02 03:43
VLAI
Summary
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
Impacted products
Vendor Product Version
MLflow MLflow Affected: 0.9.0 , ≤ * (semver)
Create a notification for this product.
lfprojects mlflow Affected: 0.9.0 , ≤ * (custom)
    cpe:2.3:a:lfprojects:mlflow:0.9.0:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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CVE-2024-37055 (GCVE-0-2024-37055)

Vulnerability from cvelistv5 – Published: 2024-06-04 12:00 – Updated: 2024-08-02 03:43
VLAI
Summary
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.24.0 or newer, enabling a maliciously uploaded pmdarima model to run arbitrary code on an end user’s system when interacted with.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
Impacted products
Vendor Product Version
MLflow MLflow Affected: 1.24.0 , ≤ * (semver)
Create a notification for this product.
lfprojects mlflow Affected: 1.24.0 , ≤ * (custom)
    cpe:2.3:a:lfprojects:mlflow:1.24.0:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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CVE-2024-37056 (GCVE-0-2024-37056)

Vulnerability from cvelistv5 – Published: 2024-06-04 12:01 – Updated: 2024-08-02 03:43
VLAI
Summary
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an end user’s system when interacted with.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
Impacted products
Vendor Product Version
MLflow MLflow Affected: 1.23.0 , ≤ * (semver)
Create a notification for this product.
lfprojects mlflow Affected: 1.23.0 , < * (custom)
    cpe:2.3:a:lfprojects:mlflow:1.23.0:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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CVE-2024-37057 (GCVE-0-2024-37057)

Vulnerability from cvelistv5 – Published: 2024-06-04 12:01 – Updated: 2024-08-02 03:43
VLAI
Summary
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with.
SSVC
Exploitation: poc Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
Impacted products
Vendor Product Version
MLflow MLflow Affected: 2.0.0rc0 , ≤ * (semver)
Create a notification for this product.
lfprojects mlflow Affected: 2.0.0rc0 , ≤ * (semver)
    cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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CVE-2024-37058 (GCVE-0-2024-37058)

Vulnerability from cvelistv5 – Published: 2024-06-04 12:01 – Updated: 2024-08-02 03:43
VLAI
Summary
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.5.0 or newer, enabling a maliciously uploaded Langchain AgentExecutor model to run arbitrary code on an end user’s system when interacted with.
SSVC
Exploitation: poc Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
Impacted products
Vendor Product Version
MLflow MLflow Affected: 2.5.0 , ≤ * (semver)
Create a notification for this product.
lfprojects mlflow Affected: 2.5.0
    cpe:2.3:a:lfprojects:mlflow:2.5.0:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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CVE-2024-37059 (GCVE-0-2024-37059)

Vulnerability from cvelistv5 – Published: 2024-06-04 12:01 – Updated: 2024-08-02 03:43
VLAI
Summary
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code on an end user’s system when interacted with.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
Impacted products
Vendor Product Version
MLflow MLflow Affected: 0.5.0 , ≤ * (semver)
Create a notification for this product.
mlflow mlflow Affected: 0.5.0 , ≤ * (custom)
    cpe:2.3:a:mlflow:mlflow:-:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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CVE-2024-37060 (GCVE-0-2024-37060)

Vulnerability from cvelistv5 – Published: 2024-06-04 12:02 – Updated: 2024-08-02 03:43
VLAI
Summary
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.27.0 or newer, enabling a maliciously crafted Recipe to execute arbitrary code on an end user’s system when run.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
Impacted products
Vendor Product Version
MLflow MLflow Affected: 1.27.0 , ≤ * (semver)
Create a notification for this product.
lfprojects mlflow Affected: 1.27.0 , ≤ * (semver)
    cpe:2.3:a:lfprojects:mlflow:1.27.0:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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CVE-2024-37062 (GCVE-0-2024-37062)

Vulnerability from cvelistv5 – Published: 2024-06-04 12:02 – Updated: 2024-08-02 03:43
VLAI
Summary
Deserialization of untrusted data can occur in versions 3.7.0 or newer of Ydata's ydata-profiling open-source library, enabling a malicously crafted report to run arbitrary code on an end user's system when loaded.
SSVC
Exploitation: poc Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
Impacted products
Vendor Product Version
YdataAI ydata-profiling Affected: 3.7.0 , ≤ * (semver)
Create a notification for this product.
ydataai ydata-profiling Affected: 0 , ≤ 3.7.0 (semver)
    cpe:2.3:a:ydataai:ydata-profiling:*:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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Mitigation

Phases: Architecture and Design, Implementation

Description:

  • If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
Mitigation

Phase: Implementation

Description:

  • When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
Mitigation

Phase: Implementation

Description:

  • Explicitly define a final object() to prevent deserialization.
Mitigation

Phases: Architecture and Design, Implementation

Description:

  • Make fields transient to protect them from deserialization.
  • An attempt to serialize and then deserialize a class containing transient fields will result in NULLs where the transient data should be. This is an excellent way to prevent time, environment-based, or sensitive variables from being carried over and used improperly.
Mitigation

Phase: Implementation

Description:

  • Avoid having unnecessary types or gadgets (a sequence of instances and method invocations that can self-execute during the deserialization process, often found in libraries) available that can be leveraged for malicious ends. This limits the potential for unintended or unauthorized types and gadgets to be leveraged by the attacker. Add only acceptable classes to an allowlist. Note: new gadgets are constantly being discovered, so this alone is not a sufficient mitigation.
Mitigation

Phases: Architecture and Design, Implementation

Description:

  • Employ cryptography of the data or code for protection. However, it's important to note that it would still be client-side security. This is risky because if the client is compromised then the security implemented on the client (the cryptography) can be bypassed.
Mitigation ID: MIT-29

Phase: Operation

Strategy: Firewall

Description:

  • Use an application firewall that can detect attacks against this weakness. It can be beneficial in cases in which the code cannot be fixed (because it is controlled by a third party), as an emergency prevention measure while more comprehensive software assurance measures are applied, or to provide defense in depth [REF-1481].
CAPEC-586: Object Injection

An adversary attempts to exploit an application by injecting additional, malicious content during its processing of serialized objects. Developers leverage serialization in order to convert data or state into a static, binary format for saving to disk or transferring over a network. These objects are then deserialized when needed to recover the data/state. By injecting a malformed object into a vulnerable application, an adversary can potentially compromise the application by manipulating the deserialization process. This can result in a number of unwanted outcomes, including remote code execution.

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