GHSA-q359-rmv4-56fg

Duplicate Advisory: PraisonAI: Plugin Auto-Discovery Executes Arbitrary Python Files Without Verification

High Withdrawn Oct 8, 2026 PyPI

Severity: 0.0 / 10; High · CVSS 4.0
Exploit chance (EPSS): —; No score published
Known exploited: No; Not in CISA's catalog
Weakness: CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: None yet; No fixed version published

Description

Duplicate Advisory

This advisory has been withdrawn because it is a duplicate of GHSA-m6wp-h223-4c8g. This link is maintained to preserve external references.

Original Description

PraisonAI (praisonaiagents) before 1.6.78 contains a remote code execution vulnerability in the plugin manager, which loads and executes arbitrary Python (.py) files from project-level and user-home .praisonai/plugins/ directories using importlib spec_from_file_location() and exec_module() without code signing, integrity verification, or sandboxing. An attacker who can write a malicious .py file to a plugin directory (for example via path traversal, a supply chain attack, or a compromised dependency) achieves arbitrary code execution when the plugin system initializes.

Affected versions

PyPI

Package	Vulnerable	Fixed in
praisonaiagents	`<= 1.6.77`	No fix yet

How to fix

No fixed version has been published yet. Until one is, remove or replace the package, or turn off or isolate the affected feature. This page updates when a fix ships.

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

Attack vector: Local
Attack complexity: Low
Attack requirements: None
Privileges required: None
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
Exploit maturity: Not defined
Scope: X

CVSS 3.1 8.4

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

Attack vector: Local
Attack complexity: Low
Privileges required: None
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].

References

Advisories

nvd.nist.gov/vuln/detail/CVE-2026-61446

Timeline

Jul 15, 2026 Published
Oct 8, 2026 Withdrawn
Oct 8, 2026 Last updated by the source

Advisory	Severity	Why it's related
GHSA-gxmw-5f7x-6g22 praisonaiagents vulnerable to arbitrary file write via unsanitized `user_id` in `FileMemory.__init__()` — path traversal to any writable location	High	Also affects praisonaiagents
GHSA-cfxv-8fw8-rwpv praisonaiagents: ast_grep_rewrite rewrites arbitrary files without the @require_approval gate enforced on every sibling mutation tool	Medium	Also affects praisonaiagents
GHSA-x44h-65qv-cw74 praisonaiagents has an SSRF protection bypass in `spider_tools._host_is_blocked()` via DNS-resolved hostnames (`127.0.0.1.nip.io`)	High	Also affects praisonaiagents
GHSA-7g3p-92qq-8wvh praisonaiagents: AgentServer declares auth_token but never enforces it on any route	High	Also affects praisonaiagents
GHSA-5r34-2g38-6569 praisonaiagents web_crawl vulnerable to SSRF via redirect-following	High	Also affects praisonaiagents
GHSA-vg6p-v9vm-6fgj praisonaiagents vulnerable to SSRF in web_crawl tool via redirect-following and DNS rebinding (validate-then-fetch gap)	High	Also affects praisonaiagents
GHSA-8hjw-25cg-g52h praisonaiagents has a `web_crawl` SSRF protection bypass via unchecked redirect targets	High	Also affects praisonaiagents
GHSA-hxmv-c4g6-5fqc PraisonAI workflow include bypasses tools.py autoload opt-in and executes included recipe code	High	Also affects praisonaiagents

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