GHSA-7qw2-w5rc-37x2 CVE-2026-57142 PYSEC-2026-3505

PraisonAI recipe workflow policy can be bypassed by declaring and YAML-approving dangerous tools outside TEMPLATE.yaml

High PyPI

Severity: 7.8 / 10; High · CVSS 3.1
Exploit chance (EPSS): 0.24%; Chance of exploitation in the next 30 days. Higher than 15.20% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-78 Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection'); CWE-863 Incorrect Authorization; CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: 4.6.61; Upgrade praisonai to this version or later

Description

Summary

PraisonAI recipe execution has a dangerous-tool policy that is supposed to block default-denied tools unless the caller explicitly passes allow_dangerous_tools=True. That policy only checks tools declared in TEMPLATE.yaml requires.tools.

For steps-based recipes, the actual execution path loads workflow.yaml with YAMLWorkflowParser. That parser resolves agent-level tools: declarations and preserves top-level approve:. Workflow.start() then installs those YAML-approved tools into the approval context.

As a result, an untrusted recipe can omit execute_command from TEMPLATE.yaml requires.tools, declare it in workflow.yaml agents.*.tools, and add top-level approve: [execute_command]. The caller did not set allow_dangerous_tools=True, but the recipe policy allows the recipe and the workflow approval path self-approves the critical shell tool.

The local PoV uses a harmless printf canary and explicitly unsets PRAISONAI_AUTO_APPROVE.

Technical Details

recipe.run() checks the recipe policy unless options["allow_dangerous_tools"] is true. _check_tool_policy() gets the required tool list from recipe_config.get_required_tools(), which is backed by TEMPLATE.yaml requires.tools.

The steps workflow execution path is separate:

_execute_steps_workflow() parses the workflow file with YAMLWorkflowParser.
YAMLWorkflowParser resolves agents.*.tools.
The same parser reads top-level approve: and stores it on workflow.approve_tools.
Workflow.start() calls set_yaml_approved_tools(approve_tools).
The approval registry treats YAML-approved tools as approved.

execute_command is listed as a default dangerous tool with critical risk and is decorated with @require_approval(risk_level="critical"). The policy gap is that recipe-level dangerous-tool enforcement does not inspect the workflow file that actually supplies and approves the tool.

Why This Is Not Intended Behavior

YAML approve: is an intended feature. This report is not claiming that workflow-level approval is inherently unintended.

The unintended behavior is that the recipe dangerous-tool policy exposes an operator-facing explicit override, allow_dangerous_tools=True, but a recipe can avoid that policy by moving the dangerous tool declaration from TEMPLATE.yaml requires.tools into the steps workflow. The recipe still runs through the standard recipe runner path, and the same workflow can self-approve the critical tool.

This conflicts with the documented safety model:

PraisonAI's approval docs describe approval as pausing an agent before a risky tool and asking a human or configured channel to allow or deny it.
The SDK approval docs describe a human-in-the-loop approval system for dangerous tool operations.
Security-environment documentation describes opt-in access for potentially dangerous operations and secure defaults for RCE prevention.
Policy-engine documentation describes policies that block dangerous operations and require approval for sensitive actions.

A control recipe that declares requires.tools: [execute_command] is denied with:

Tool 'execute_command' is denied by default. Use allow_dangerous_tools=True to override.

The bypass recipe uses the same tool but omits it from requires.tools; it passes policy and reaches the recipe runner's dry-run state.

PoV

Run:

python3 poc/poc.py

Expected output:

{
  "ok": true,
  "control_policy": "Tool 'execute_command' is denied by default. Use allow_dangerous_tools=True to override.",
  "control_recipe_status": "policy_denied",
  "bypass_policy": null,
  "bypass_recipe_dry_run_status": "dry_run",
  "workflow_approve_tools": [
    "execute_command"
  ],
  "runner_tool_names": [
    "execute_command"
  ],
  "command_stdout": "poc",
  "operator_env_auto_approve": null
}

The PoV creates two temporary recipes:

A control recipe with TEMPLATE.yaml requires.tools: [execute_command]. recipe.run() returns policy_denied.
A bypass recipe with no dangerous tools in TEMPLATE.yaml, but with workflow.yaml declaring execute_command under an agent and approve: [execute_command]. recipe.run(..., dry_run=True) reaches dry_run, and the same parser/approval context permits a harmless `printf poc.

PoC

The PoV section above contains the local reproduction command, input, and decisive output.

Impact

If an operator runs an untrusted recipe, or exposes the recipe runner to users who can choose recipe names/URIs, the recipe can self-authorize a default-denied critical shell tool without the operator setting allow_dangerous_tools=True.

Successful exploitation lets the workflow run execute_command with the privileges of the PraisonAI process if the agent reaches the tool call. The exact trigger depends on the workflow and model/tool-call path, but the policy boundary is already bypassed before execution.

This can affect both local CLI use and HTTP recipe-runner deployments. The HTTP recipe runner defaults to localhost/no-auth and requires auth for non-localhost binding, so this report uses local/UI-required severity rather than claiming an unauthenticated network RCE by default.

The local HTTP sidecar documentation also frames the sidecar as a localhost REST API for local/polyglot integration. If a deployment exposes that API to authenticated users who can choose recipe names or URIs, the same policy bypass can become an authenticated remote recipe-execution issue, but that is not the default severity claim.

Severity

Suggested severity: High.

Suggested Fix

Normalize and validate the actual workflow tool graph before recipe execution:

Parse the selected workflow file before or during _check_tool_policy().
Include workflow.yaml agents.*.tools, roles.*.tools, included recipes, and other workflow-resolved tool lists in the dangerous-tool policy.
Treat approve: as an operator-supplied approval policy, not a recipe-controlled bypass of the recipe-level dangerous-tool gate.
If approve: remains recipe-controlled, ignore dangerous/default-denied tool entries unless the caller passed allow_dangerous_tools=True or an explicit external policy allowed that exact tool.
Add regression tests for:
- dangerous tool in TEMPLATE.yaml requires.tools is denied;
- dangerous tool in workflow.yaml agents.*.tools is also denied;
- approve: [execute_command] does not bypass the recipe policy;
- allow_dangerous_tools=True keeps the intended opt-in behavior.

Affected Package/Versions

Repository: MervinPraison/PraisonAI
Package: praisonai
Components:
- src/praisonai/praisonai/recipe/core.py
- src/praisonai/praisonai/recipe/models.py
- src/praisonai-agents/praisonaiagents/workflows/yaml_parser.py
- src/praisonai-agents/praisonaiagents/workflows/workflows.py
- src/praisonai-agents/praisonaiagents/approval/registry.py

Validated affected:

current main 2f9677abb2ea68eab864ee8b6a828fd0141612e1
v4.6.57
v4.6.56
v4.6.10
v4.6.9
v4.5.128
v4.5.120
v4.5.96
v4.5.87

Suggested affected range: >= 4.5.87, <= 4.6.57.

PyPI lists PraisonAI 4.6.57 as the latest release on 2026-06-13.

Earlier tested tags through v4.5.85 failed in this source checkout before the tested workflow path due an unrelated praisonaiagents.output.models import error. They are not claimed fixed or unaffected.

Advisory History

Checked visible PraisonAI advisories and prior submissions for the same root cause, affected entrypoint, and exploit preconditions. No exact duplicate is identified in this report text. Adjacent advisories, where relevant, are listed in References or discussed above.

Affected versions

PyPI

Package	Vulnerable	Fixed in
praisonai	`>= 4.5.87, < 4.6.61`	`4.6.61`

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 → `4.6.61`

pip

pip install --upgrade "praisonai>=4.6.61"

poetry

poetry add "praisonai>=4.6.61"

uv

uv add "praisonai>=4.6.61"

Severity breakdown

CVSS 3.1 7.8

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

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

Weakness

CWE-78: Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')

The product constructs all or part of an OS command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended OS command when it is sent to a downstream component.

How to prevent it

Architecture and Design: If at all possible, use library calls rather than external processes to recreate the desired functionality.
Architecture and Design, Operation: Run the code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which files can be accessed in a particular directory or which commands can be executed by the software. OS-level examples include the Unix chroot jail, AppArmor, and SELinux. In general, managed code may provide some protection. For example, java.io.FilePermission in the Java SecurityManager allows the software to specify restrictions on file operations. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of the application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
Architecture and Design: For any data that will be used to generate a command to be executed, keep as much of that data out of external control as possible. For example, in web applications, this may require storing the data locally in the session's state instead of sending it out to the client in a hidden form field.
Architecture and Design: For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
Architecture and Design: Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid. For example, consider using the ESAPI Encoding control [REF-45] or a similar tool, library, or framework. These will help the programmer encode outputs in a manner less prone to error.
Implementation: While it is risky to use dynamically-generated query strings, code, or commands that mix control and data together, sometimes it may be unavoidable. Properly quote arguments and escape any special characters within those arguments. The most conservative approach is to escape or filter all characters that do not pass an extremely strict allowlist (such as everything that is not alphanumeric or white space). If some special characters are still needed, such as white space, wrap each argument in quotes after the escaping/filtering step. Be careful of argument injection (CWE-88).
Implementation: If the program to be executed allows arguments to be specified within an input file or from standard input, then consider using that mode to pass arguments instead of the command line.
Architecture and Design: If available, use structured mechanisms that automatically enforce the separation between data and code. These mechanisms may be able to provide the relevant quoting, encoding, and validation automatically, instead of relying on the developer to provide this capability at every point where output is generated. Some languages offer multiple functions that can be used to invoke commands. Where possible, identify any function that invokes a command shell using a single string, and replace it with a function that requires individual arguments. These functions typically perform appropriate quoting and filtering of arguments. For example, in C, the system() function accepts a string that contains the entire command to be executed, whereas execl(), execve(), and others require an array of strings, one for each argument. In Windows, CreateProcess() only accepts one command at a time. In Perl, if system() is provided with an array of arguments, then it will quote each of the arguments.
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. When constructing OS command strings, use stringent allowlists that limit the character set based on the expected value of the parameter in the request. This will indirectly limit the scope of an attack, but this technique is less important than proper output encoding and escaping. Note that proper output encoding, escaping, and quoting is the most effective solution for preventing OS command injection, although input validation may provide some defense-in-depth. This is because it effectively limits what will appear in output. Input validation will not always prevent OS command injection, especially if you are required to support free-form text fields that could contain arbitrary characters. For example, when invoking a mail program, you might need to allow the subject field to contain otherwise-dangerous inputs like ";" and ">" characters, which would ne
Architecture and Design: When the set of acceptable objects, such as filenames or URLs, is limited or known, create a mapping from a set of fixed input values (such as numeric IDs) to the actual filenames or URLs, and reject all other inputs.
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: Ensure that error messages only contain minimal details that are useful to the intended audience and no one else. The messages need to strike the balance between being too cryptic (which can confuse users) or being too detailed (which may reveal more than intended). The messages should not reveal the methods that were used to determine the error. Attackers can use detailed information to refine or optimize their original attack, thereby increasing their chances of success. If errors must be captured in some detail, record them in log messages, but consider what could occur if the log messages can be viewed by attackers. Highly sensitive information such as passwords should never be saved to log files. Avoid inconsistent messaging that might accidentally tip off an attacker about internal state, such as whether a user account exists or not. In the context of OS Command Injection, error information passed back to the user might reveal whether an OS command is being executed and possibly which command is being used.
Operation: Use runtime policy enforcement to create an allowlist of allowable commands, then prevent use of any command that does not appear in the allowlist. Technologies such as AppArmor are available to do this.
Operation: 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].
Architecture and Design, Operation: Run your code using the lowest privileges that are required to accomplish the necessary tasks [REF-76]. If possible, create isolated accounts with limited privileges that are only used for a single task. That way, a successful attack will not immediately give the attacker access to the rest of the software or its environment. For example, database applications rarely need to run as the database administrator, especially in day-to-day operations.
Operation, Implementation: When using PHP, configure the application so that it does not use register_globals. During implementation, develop the application so that it does not rely on this feature, but be wary of implementing a register_globals emulation that is subject to weaknesses such as CWE-95, CWE-621, and similar issues.

CWE-863: Incorrect Authorization

The product performs an authorization check when an actor attempts to access a resource or perform an action, but it does not correctly perform the check.

How to prevent it

Architecture and Design: Divide the product into anonymous, normal, privileged, and administrative areas. Reduce the attack surface by carefully mapping roles with data and functionality. Use role-based access control (RBAC) [REF-229] to enforce the roles at the appropriate boundaries. Note that this approach may not protect against horizontal authorization, i.e., it will not protect a user from attacking others with the same role.
Architecture and Design: Ensure that access control checks are performed related to the business logic. These checks may be different than the access control checks that are applied to more generic resources such as files, connections, processes, memory, and database records. For example, a database may restrict access for medical records to a specific database user, but each record might only be intended to be accessible to the patient and the patient's doctor [REF-7].
Architecture and Design: Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid. For example, consider using authorization frameworks such as the JAAS Authorization Framework [REF-233] and the OWASP ESAPI Access Control feature [REF-45].
Architecture and Design: For web applications, make sure that the access control mechanism is enforced correctly at the server side on every page. Users should not be able to access any unauthorized functionality or information by simply requesting direct access to that page. One way to do this is to ensure that all pages containing sensitive information are not cached, and that all such pages restrict access to requests that are accompanied by an active and authenticated session token associated with a user who has the required permissions to access that page.
System Configuration, Installation: Use the access control capabilities of your operating system and server environment and define your access control lists accordingly. Use a "default deny" policy when defining these ACLs.

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

Jun 18, 2026 Published
Oct 29, 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-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
GHSA-rg5q-pp8p-f7jm PraisonAI: Webhook SSRF via DNS fail-open in `JobSubmitRequest.validate_webhook_url()` — bypass of CVE-2026-40114	High	Also affects praisonai

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Description

Summary

Technical Details

Why This Is Not Intended Behavior

PoV

PoC

Impact

Severity

Suggested Fix

Affected Package/Versions

Advisory History

Affected versions

PyPI

How to fix

praisonai PyPI → 4.6.61

Severity breakdown

CVSS 3.1 7.8

Weakness

CWE-78: Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')

CWE-863: Incorrect Authorization

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

References

Advisories

Packages

Other links

Timeline

Related advisories

praisonai PyPI → `4.6.61`