GHSA-g985-wjh9-qxxc CVE-2026-40287 PYSEC-2026-2914 PYSEC-2026-2947

PraisonAI Vulnerable to RCE via Automatic tools.py Import

High PyPI

Severity: 8.4 / 10; High · CVSS 3.1
Exploit chance (EPSS): 0.25%; Chance of exploitation in the next 30 days. Higher than 15.96% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-426 Untrusted Search Path; CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: Available; 2 packages have a fixed version

Description

PraisonAI automatically imports ./tools.py from the current working directory when launching certain components. This includes call.py, tool_resolver.py, and CLI tool-loading paths.

A malicious tools.py placed in the process working directory is executed immediately, allowing arbitrary Python code execution in the host environment.

Affected Code

call.py → import_tools_from_file()
tool_resolver.py → _load_local_tools()
tools.py → local tool import flow

PoC

Create tools.py in the directory where PraisonAI is launched:

# tools.py
import os
os.system("echo pwned > /tmp/pwned.txt")

Run any PraisonAI component that loads local tools, for example:

praisonai workflow run safe.yaml

Reproduction Steps

Create a malicious tools.py in the current working directory.
Start PraisonAI or invoke a CLI command that loads local tools.
Verify that /tmp/pwned.txt or the malicious command output exists.

Impact

An attacker who can place or influence tools.py in the working directory can execute arbitrary code in the PraisonAI process, compromising the host and any connected data.

Reporter: Lakshmikanthan K (letchupkt)

Affected versions

PyPI

Package	Vulnerable	Fixed in
praisonai	`< 4.5.139`	`4.5.139`
praisonaiagents	`< 1.5.140`	`1.5.140`

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.

praisonai PyPI → `4.5.139`

pip

pip install --upgrade "praisonai>=4.5.139"

poetry

poetry add "praisonai>=4.5.139"

uv

uv add "praisonai>=4.5.139"

praisonaiagents PyPI → `1.5.140`

pip

pip install --upgrade "praisonaiagents>=1.5.140"

poetry

poetry add "praisonaiagents>=1.5.140"

uv

uv add "praisonaiagents>=1.5.140"

Severity breakdown

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-426: Untrusted Search Path

The product searches for critical resources using an externally-supplied search path that can point to resources that are not under the product's direct control.

How to prevent it

Architecture and Design, Implementation: Hard-code the search path to a set of known-safe values (such as system directories), or only allow them to be specified by the administrator in a configuration file. Do not allow these settings to be modified by an external party. Be careful to avoid related weaknesses such as CWE-426 and CWE-428.
Implementation: When invoking other programs, specify those programs using fully-qualified pathnames. While this is an effective approach, code that uses fully-qualified pathnames might not be portable to other systems that do not use the same pathnames. The portability can be improved by locating the full-qualified paths in a centralized, easily-modifiable location within the source code, and having the code refer to these paths.
Implementation: Remove or restrict all environment settings before invoking other programs. This includes the PATH environment variable, LD_LIBRARY_PATH, and other settings that identify the location of code libraries, and any application-specific search paths.
Implementation: Check your search path before use and remove any elements that are likely to be unsafe, such as the current working directory or a temporary files directory.
Implementation: Use other functions that require explicit paths. Making use of any of the other readily available functions that require explicit paths is a safe way to avoid this problem. For example, system() in C does not require a full path since the shell can take care of it, while execl() and execv() require a full path.

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

Packages

Timeline

Apr 10, 2026 Published
Oct 26, 2026 Last updated by the source
Nov 1, 2026 Latest EPSS score

Advisory	Severity	Why it's related
GHSA-26mh-57q7-jfvr PraisonAI vulnerable to Remote Code Execution via Broken AST Sandbox	High	Also affects praisonai
GHSA-6g6r-q6gw-w8fg PraisonAI has a Browser Server WebSocket origin validation bypass via unanchored regex (patch bypass of CVE-2026-40289 / GHSA-8x8f-54wf-vv92)	Critical	Also affects praisonai
GHSA-pvph-5j39-v8qc PraisonAI: Origin-validation bypass (startswith prefix match) enables unauthenticated cross-site request forgery against the PraisonAI MCP HTTP server	High	Also affects praisonai
GHSA-gfq8-hmph-9gjv PraisonAI: Authentication fail-open in Recipe server allows unauthenticated access when API key or JWT auth is configured without a secret	High	Also affects praisonai
GHSA-2jgc-f764-c5r2 PraisonAI: [Auth Bypass] PraisonAI async Jobs API (`/api/v1/runs`) has no authentication — unauthenticated job execution, result theft, cancel and delete	High	Also affects praisonai
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-pvxx-r596-f5qj PraisonAI: `--api-key` flag on `praisonai serve` is not properly enforced	High	Also affects praisonai
GHSA-hmfx-4v44-9qw9 PraisonAI vulnerable to Server-Side Request Forgery via DNS rebinding bypass in webhook_url validation	Medium	Also affects praisonai

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