GHSA-wcjw-3v6p-4v3r CVE-2024-45846 PYSEC-2024-77

MindsDB Eval Injection vulnerability

High PyPI

Severity: 0.0 / 10; High · CVSS 4.0
Exploit chance (EPSS): 2.15%; Chance of exploitation in the next 30 days. Higher than 82.12% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-94 Improper Control of Generation of Code ('Code Injection'); CWE-95 Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
Fix: 24.7.4.1; Upgrade mindsdb to this version or later

Description

An arbitrary code execution vulnerability exists in versions 23.10.3.0 up to 24.7.4.1 of the MindsDB platform, when the Weaviate integration is installed on the server. If a specially crafted ‘SELECT WHERE’ clause containing Python code is run against a database created with the Weaviate engine, the code will be passed to an eval function and executed on the server.

Affected versions

PyPI

Package	Vulnerable	Fixed in
mindsdb	`>= 23.10.3.0, < 24.7.4.1`	`24.7.4.1`

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

mindsdb PyPI → `24.7.4.1`

pip

pip install --upgrade "mindsdb>=24.7.4.1"

poetry

poetry add "mindsdb>=24.7.4.1"

uv

uv add "mindsdb>=24.7.4.1"

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Attack vector: Network
Attack complexity: Low
Attack requirements: None
Privileges required: Low
User interaction: None
Confidentiality (vulnerable system): High
Integrity (vulnerable system): High
Availability (vulnerable system): High
Confidentiality (subsequent systems): None
Integrity (subsequent systems): None
Availability (subsequent systems): None

CVSS 3.1 8.8

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Attack vector: Network
Attack complexity: Low
Privileges required: Low
User interaction: None
Scope: Unchanged
Confidentiality: High
Integrity: High
Availability: High

Weakness

CWE-94: Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

How to prevent it

Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

CWE-95: Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')

The product receives input from an upstream component, but it does not neutralize or incorrectly neutralizes code syntax before using the input in a dynamic evaluation call (e.g. "eval").

How to prevent it

Architecture and Design, Implementation: If possible, refactor your code so that it does not need to use eval() at all.
Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
Implementation: Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180, CWE-181). Make sure that your application does not inadvertently decode the same input twice (CWE-174). Such errors could be used to bypass allowlist schemes by introducing dangerous inputs after they have been checked. Use libraries such as the OWASP ESAPI Canonicalization control. Consider performing repeated canonicalization until your input does not change any more. This will avoid double-decoding and similar scenarios, but it might inadvertently modify inputs that are allowed to contain properly-encoded dangerous content.
Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

References

Advisories

Exploits and evidence

hiddenlayer.com/sai-security-advisory/2024-09-mindsdb

Packages

github.com/mindsdb/mindsdb

Timeline

Sep 12, 2024 Published
Jun 17, 2026 Last updated by the source
Oct 31, 2026 Latest EPSS score

Advisory	Severity	Why it's related
GHSA-9f6m-65v9-x9g2 MindsDB has an Improper Access Control Issue	High	Also affects mindsdb
GHSA-4894-xqv6-vrfq MindsDB: Path Traversal in /api/files Leading to Remote Code Execution	High	Also affects mindsdb
GHSA-6xw9-2p64-7622 MindsDB affected by a SSRF vulnerability	High	Also affects mindsdb
GHSA-qqhf-pm3j-96g7 MindsDB has improper sanitation of filepath that leads to information disclosure and DOS	Critical	Also affects mindsdb
GHSA-32fj-r8qw-r8w8 MindsDB Cross-site Scripting vulnerability	Medium	Also affects mindsdb
GHSA-crmg-rp64-5cm3 MindsDB Eval Injection vulnerability	High	Also affects mindsdb
GHSA-wf9g-c67g-h4ch MindsDB Eval Injection vulnerability	High	Also affects mindsdb
GHSA-v6g6-3cm3-vf6c MindsDB Eval Injection vulnerability	High	Also affects mindsdb

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