Skip to content

npm PraisonAI AgentLoop onToolCall approval runs after tool execution

High severity GitHub Reviewed Published Jun 17, 2026 in MervinPraison/PraisonAI • Updated Jun 18, 2026

Package

npm praisonai (npm)

Affected versions

>= 1.4.0, <= 1.7.1

Patched versions

1.7.2

Description

Summary

The published npm package praisonai exports createAgentLoop(), whose onToolCall callback is documented and exampled as an approval hook. The implementation calls PraisonAI's generateText() wrapper with the caller's executable tools first, receives toolResults, and only then calls onToolCall().

Because AI SDK generateText() executes tools with an execute function as part of the generation call, onToolCall can deny a tool only after the sensitive side effect has already happened. PraisonAI then returns finishReason: "tool_rejected", which is a false security signal: the rejected tool already ran.

The PoV is deterministic and local-only. It uses mock AI SDK modules, no live model call, no API key, and no network target. The tool increments an in-memory counter rather than touching the filesystem or executing commands.

Technical Details

In src/praisonai-ts/src/ai/agent-loop.ts, the public config says:

/** On tool call callback (for approval) */
onToolCall?: (toolCall: ToolCallInfo) => Promise<boolean>;

The inline approval example also asks a user for approval and returns the decision:

onToolCall: async (toolCall) => {
  const approved = await askUserForApproval(toolCall);
  return approved;
}

However, AgentLoop.step() calls generateText() with the executable tools before invoking onToolCall:

const result = await generateText({
  model: this.config.model,
  messages: this.messages as any,
  tools: this.config.tools,
  maxSteps: 1,
});

It then materializes toolResults:

toolResults: result.toolResults.map(tr => ({
  toolCallId: tr.toolCallId,
  toolName: tr.toolName,
  result: tr.result,
})),

Only afterward does the approval callback run:

if (this.config.onToolCall) {
  for (const toolCall of step.toolCalls) {
    const approved = await this.config.onToolCall(toolCall);
    if (!approved) {
      this.complete = true;
      step.finishReason = 'tool_rejected';
      break;
    }
  }
}

src/praisonai-ts/src/ai/generate-text.ts forwards the caller's tools directly to AI SDK:

const result = await sdk.generateText({
  model,
  ...
  tools: options.tools,
  maxSteps: options.maxSteps,
  ...
});

AI SDK documents that generateText() "generates text and calls tools", and that tools with an execute function run automatically unless approval is handled before execution with needsApproval.

The published npm:praisonai@1.7.1 dist files preserve the same order:

  • dist/ai/agent-loop.js lines 150-157 call generateText() with executable tools.
  • lines 162-171 materialize toolResults.
  • lines 183-195 call onToolCall() and set tool_rejected afterward.

Why This Is Not Intended Behavior

This is not a trust-model-only issue. PraisonAI explicitly labels onToolCall as an approval callback and shows an approval example. A user who returns false from that callback expects the tool not to run.

It also conflicts with the AI SDK execution model PraisonAI wraps:

  • AI SDK generateText() executes tools that include an execute function.
  • AI SDK approval is a pre-execution boundary (needsApproval), not a post-execution notification.
  • AI SDK loop control documentation treats "a tool call needs approval" as a condition that stops or pauses the loop before executing the tool.

PraisonAI's current behavior instead creates a post-execution audit hook while naming and documenting it as approval.

PoV

Run from a local reproduction checkout:

node poc/pov_poc.js 1.7.1

Expected output includes:

{
  "praisonaiVersion": "1.7.1",
  "createAgentLoopExported": true,
  "eventOrder": ["tool-executed", "approval-denied"],
  "sideEffects": 1,
  "finishReason": "tool_rejected",
  "toolCallCount": 1,
  "toolResultCount": 1,
  "rejectedAfterExecution": true,
  "vulnerable": true,
  "patchedControl": {
    "order": ["approval-denied"],
    "sideEffects": 0,
    "toolCallCount": 1,
    "toolResultCount": 0,
    "blocksBeforeExecution": true
  }
}

The PoV installs npm:praisonai@1.7.1 into a temporary project and supplies mock ai and @ai-sdk/openai modules. The mocked generateText() returns one tool-call intent and executes a supplied execute handler if present. This keeps the proof deterministic and isolates PraisonAI's ordering bug.

The vulnerable run uses createAgentLoop() with:

  • a dangerousWrite tool whose execute() handler increments an in-memory side-effect counter and records tool-executed;
  • an onToolCall approval callback that always returns false and records approval-denied.

The observed order is:

tool-executed > approval-denied

That proves denial happens after execution. The toolResults array contains the tool's result even though PraisonAI reports finishReason: "tool_rejected".

The patched-control comparison strips executable handlers before the model step, requests approval on the tool-call intent, and only executes if approval succeeds. With the same denial decision, the control output is:

approval-denied
sideEffects = 0
toolResultCount = 0

PoC

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

Impact

Any application using npm PraisonAI createAgentLoop() with onToolCall as a human-in-the-loop or policy approval boundary can execute denied tools.

If the application exposes the agent loop to lower-trust prompts or users and registers powerful tools, an attacker can cause the model to call a tool that the approval callback denies. The denial occurs too late. Depending on the registered tool, impact can include file modification, command execution, external API calls, data mutation, credential use, or other side effects with the privileges of the PraisonAI process.

The report does not claim that npm PraisonAI exposes this as a default network service. It is a library-level approval-boundary bypass in the exported TypeScript agent-loop API.

Severity

Suggested severity: High.

Rationale:

  • AV: common deployment pattern is an application exposing agent prompts over a network.
  • AC: attacker only needs to induce a tool call.
  • PR: conservative base score assumes the attacker can submit prompts to the application.
  • UI: no additional operator action is needed for the tool to execute before denial; even a denial callback is too late.
  • S: impact is in the PraisonAI-hosting application process.
  • C/I/A: depends on registered tools; shell/file/API tools can affect confidentiality, integrity, and availability.

If maintainers score only local scripts that process untrusted repositories or prompts, AV:L may be reasonable. If they score public unauthenticated prompt endpoints built on this API, PR:N may be reasonable.

Suggested Fix

Do not pass executable tool handlers into generateText() before approval.

One safe shape:

  1. Convert configured tools into intent-only tool definitions without execute.
  2. Call generateText() to obtain the model's tool-call intent.
  3. Invoke onToolCall(toolCall) before any side effect.
  4. Execute the selected tool only if approval returns true.
  5. Append approved tool results to the conversation and continue the loop.

Alternatively, if PraisonAI wants to delegate approval to AI SDK v6, translate onToolCall into per-tool needsApproval semantics so AI SDK pauses before calling execute.

Regression tests should include:

  • onToolCall returns false and the tool execute() counter remains zero;
  • onToolCall returns true and the tool executes exactly once;
  • tool_rejected is never reported together with a tool result produced by the denied tool;
  • streaming and non-streaming loop variants use the same approval ordering if added later.

Affected Package/Versions

  • Repository: MervinPraison/PraisonAI
  • Package: npm:praisonai
  • Component: TypeScript AgentLoop
  • Current head validated: 1ad58ca02975ff1398efeda694ea2ab78f20cf3e
  • Current tag validated: v4.6.58
  • Latest npm package validated: 1.7.1

Suggested affected range:

npm:praisonai >= 1.4.0, <= 1.7.1

Selected version sweep:

  • 1.0.0: package main cannot be required in the selected test environment.
  • 1.2.0: createAgentLoop is not exported.
  • 1.3.6: createAgentLoop is not exported.
  • 1.4.0: vulnerable.
  • 1.5.0: vulnerable.
  • 1.5.4: vulnerable.
  • 1.6.0: vulnerable.
  • 1.7.0: vulnerable.
  • 1.7.1: vulnerable.

Advisory History

This is distinct from known and previously submitted PraisonAI issues:

  • GHSA-ffp3-3562-8cv3 covers Python praisonaiagents approval cache keyed by tool name rather than invocation arguments.
  • GHSA-qwgj-rrpj-75xm covers Python Chainlit UI overriding configured approval mode with auto.
  • GHSA-63v4-w882-g4x2 / poc covers Python HTTPApproval approval-page XSS.
  • poc covers npm TypeScript AgentOS missing authentication.
  • poc covers npm TypeScript codeMode sandbox escape.
  • poc covers npm TypeScript MCPServer missing authentication.

No visible local or GitHub advisory covers npm TypeScript AgentLoop.onToolCall executing after tool results already exist.

References

@MervinPraison MervinPraison published to MervinPraison/PraisonAI Jun 17, 2026
Published to the GitHub Advisory Database Jun 18, 2026
Reviewed Jun 18, 2026
Last updated Jun 18, 2026

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

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

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

EPSS score

Weaknesses

Protection Mechanism Failure

The product does not use or incorrectly uses a protection mechanism that provides sufficient defense against directed attacks against the product. Learn more on MITRE.

Missing Authorization

The product does not perform an authorization check when an actor attempts to access a resource or perform an action. Learn more on MITRE.

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. Learn more on MITRE.

CVE ID

No known CVE

GHSA ID

GHSA-h2w2-v7j6-xqm4

Credits

Loading Checking history
See something to contribute? Suggest improvements for this vulnerability.