{"id":"PYSEC-2026-2946","summary":"PraisonAI: Coarse-Grained Tool Approval Cache Bypasses Per-Invocation Consent for Shell Commands","details":"## Summary\n\nThe approval system in PraisonAI Agents caches tool approval decisions by tool name only, not by invocation arguments. Once a user approves `execute_command` for any command (e.g., `ls -la`), all subsequent `execute_command` calls in that execution context bypass the approval prompt entirely. Combined with `os.environ.copy()` passing all process environment variables to subprocesses, this allows an LLM agent (potentially via prompt injection) to silently exfiltrate API keys and credentials without further user consent.\n\n## Details\n\nThe `require_approval` decorator in `src/praisonai-agents/praisonaiagents/approval/__init__.py:176-178` checks approval status by tool name only:\n\n```python\n@wraps(func)\ndef wrapper(*args, **kwargs):\n    if is_already_approved(tool_name):   # line 177 — checks only tool_name\n        return func(*args, **kwargs)     # line 178 — bypasses ALL approval\n```\n\nThe `mark_approved` function in `registry.py:144-147` stores only the tool name string:\n\n```python\ndef mark_approved(self, tool_name: str) -\u003e None:\n    approved = self._approved_context.get(set())\n    approved.add(tool_name)              # stores \"execute_command\", not args\n    self._approved_context.set(approved)\n```\n\nThe approval context is never cleared during agent execution — `clear_approved()` exists (`registry.py:152`) but is never called in the agent's tool execution path (`agent/tool_execution.py`).\n\nMeanwhile, the `ConsoleBackend` UI at `backends.py:95-96` misleads the user:\n\n```python\nreturn Confirm.ask(\n    f\"Do you want to execute this {request.risk_level} risk tool?\",\n    # \"this\" implies per-invocation approval\n)\n```\n\nThe UI displays the specific command arguments (lines 81-85), creating a reasonable expectation that the user is approving only that specific invocation.\n\nAdditionally, `shell_tools.py:77` passes the full process environment to every subprocess:\n\n```python\nprocess_env = os.environ.copy()  # includes OPENAI_API_KEY, etc.\n```\n\nThere is no command filtering, blocklist, or environment variable sanitization in the shell tools module.\n\n## PoC\n\n```python\nfrom praisonaiagents import Agent\nfrom praisonaiagents.tools.shell_tools import execute_command\n\n# Step 1: Create agent with shell tool\nagent = Agent(\n    name=\"worker\",\n    instructions=\"You are a helpful assistant.\",\n    tools=[execute_command]\n)\n\n# Step 2: Agent requests benign command — user sees Rich panel:\n#   Function: execute_command\n#   Risk Level: CRITICAL\n#   Arguments:\n#     command: ls -la\n#   \"Do you want to execute this critical risk tool?\" [y/N]\n# User approves → mark_approved(\"execute_command\") is called\n\n# Step 3: All subsequent execute_command calls bypass approval silently:\n# execute_command(command=\"env\")\n#   → returns ALL environment variables (OPENAI_API_KEY, AWS_SECRET_ACCESS_KEY, etc.)\n#   → NO approval prompt shown\n\n# Step 4: Targeted extraction also bypasses approval:\n# execute_command(command=\"printenv OPENAI_API_KEY\")\n#   → returns the specific API key\n#   → NO approval prompt shown\n\n# Verification: check the approval cache\nfrom praisonaiagents.approval import is_already_approved\n# After approving \"ls -la\":\n# is_already_approved(\"execute_command\") → True\n# Any execute_command call now returns immediately at __init__.py:177-178\n```\n\n## Impact\n\n- **Secret exfiltration**: An LLM agent (or one subjected to prompt injection) can dump all process environment variables after a single benign command approval. Common secrets include `OPENAI_API_KEY`, `AWS_SECRET_ACCESS_KEY`, `DATABASE_URL`, and any other credentials passed via environment.\n- **Misleading consent UI**: The console prompt displays specific arguments and uses language (\"this tool\") that implies per-invocation consent, but the system grants session-wide blanket approval.\n- **No expiration or scope**: The approval cache uses a `ContextVar` that persists for the entire agent execution context with no timeout, no command-count limit, and no clearing between tool calls.\n- **No environment filtering**: `os.environ.copy()` passes every environment variable to subprocesses without filtering sensitive patterns.\n\n## Recommended Fix\n\n1. **Per-invocation approval for critical tools** — store a hash of `(tool_name, arguments)` instead of just `tool_name`, or require re-approval for each invocation of critical-risk tools:\n\n```python\n# In registry.py — change mark_approved/is_already_approved:\nimport hashlib, json\n\ndef mark_approved(self, tool_name: str, arguments: dict = None) -\u003e None:\n    approved = self._approved_context.get(set())\n    risk = self._risk_levels.get(tool_name)\n    if risk == \"critical\" and arguments:\n        key = f\"{tool_name}:{hashlib.sha256(json.dumps(arguments, sort_keys=True).encode()).hexdigest()}\"\n    else:\n        key = tool_name\n    approved.add(key)\n    self._approved_context.set(approved)\n\ndef is_already_approved(self, tool_name: str, arguments: dict = None) -\u003e bool:\n    approved = self._approved_context.get(set())\n    risk = self._risk_levels.get(tool_name)\n    if risk == \"critical\" and arguments:\n        key = f\"{tool_name}:{hashlib.sha256(json.dumps(arguments, sort_keys=True).encode()).hexdigest()}\"\n        return key in approved\n    return tool_name in approved\n```\n\n2. **Filter environment variables** in `shell_tools.py`:\n\n```python\nSENSITIVE_PATTERNS = ('_KEY', '_SECRET', '_TOKEN', '_PASSWORD', '_CREDENTIAL')\n\nprocess_env = {\n    k: v for k, v in os.environ.items()\n    if not any(p in k.upper() for p in SENSITIVE_PATTERNS)\n}\nif env:\n    process_env.update(env)\n```","aliases":["CVE-2026-56074","GHSA-ffp3-3562-8cv3"],"modified":"2026-09-18T02:00:03.605686787Z","published":"2026-07-13T14:36:52.273166Z","references":[{"type":"WEB","url":"https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-ffp3-3562-8cv3"},{"type":"ADVISORY","url":"https://nvd.nist.gov/vuln/detail/CVE-2026-56074"},{"type":"PACKAGE","url":"https://github.com/MervinPraison/PraisonAI"},{"type":"WEB","url":"https://github.com/MervinPraison/PraisonAI/releases/tag/v4.5.128"},{"type":"WEB","url":"https://www.vulncheck.com/advisories/praisonai-tool-approval-cache-bypass-via-coarse-grained-caching"},{"type":"PACKAGE","url":"https://pypi.org/project/praisonaiagents"},{"type":"ADVISORY","url":"https://github.com/advisories/GHSA-ffp3-3562-8cv3"}],"affected":[{"package":{"name":"praisonaiagents","ecosystem":"PyPI","purl":"pkg:pypi/praisonaiagents"},"ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"4.5.128"}]}],"versions":["0.0.1","0.0.10","0.0.100","0.0.101","0.0.102","0.0.103","0.0.104","0.0.105","0.0.106","0.0.107","0.0.108","0.0.109","0.0.11","0.0.110","0.0.111","0.0.112","0.0.113","0.0.114","0.0.115","0.0.116","0.0.117","0.0.118","0.0.119","0.0.12","0.0.120","0.0.121","0.0.122","0.0.123","0.0.124","0.0.125","0.0.126","0.0.127","0.0.128","0.0.129","0.0.13","0.0.130","0.0.131","0.0.132","0.0.133","0.0.134","0.0.135","0.0.136","0.0.137","0.0.138","0.0.139","0.0.14","0.0.140","0.0.141","0.0.142","0.0.143","0.0.144","0.0.145","0.0.146","0.0.147","0.0.148","0.0.149","0.0.15","0.0.150","0.0.151","0.0.152","0.0.153","0.0.154","0.0.155","0.0.156","0.0.157","0.0.158","0.0.159","0.0.16","0.0.160","0.0.161","0.0.162","0.0.163","0.0.164","0.0.165","0.0.166","0.0.167","0.0.168","0.0.169","0.0.17","0.0.170","0.0.171","0.0.172","0.0.173","0.0.174","0.0.175","0.0.176","0.0.177","0.0.178","0.0.179","0.0.18","0.0.180","0.0.181","0.0.182","0.0.183","0.0.184","0.0.185","0.0.187","0.0.188","0.0.189","0.0.19","0.0.190","0.0.191","0.0.192","0.0.193","0.0.194","0.0.195","0.0.196","0.0.197","0.0.198","0.0.199","0.0.2","0.0.20","0.0.21","0.0.22","0.0.23","0.0.24","0.0.25","0.0.26","0.0.27","0.0.28","0.0.29","0.0.3","0.0.30","0.0.31","0.0.32","0.0.33","0.0.34","0.0.35","0.0.36","0.0.37","0.0.38","0.0.39","0.0.4","0.0.40","0.0.41","0.0.42","0.0.43","0.0.44","0.0.45","0.0.46","0.0.47","0.0.48","0.0.49","0.0.5","0.0.50","0.0.51","0.0.52","0.0.53","0.0.54","0.0.56","0.0.57","0.0.58","0.0.59","0.0.6","0.0.60","0.0.61","0.0.62","0.0.63","0.0.64","0.0.65","0.0.66","0.0.67","0.0.68","0.0.69","0.0.7","0.0.70","0.0.71","0.0.72","0.0.73","0.0.74","0.0.75","0.0.76","0.0.77","0.0.78","0.0.79","0.0.8","0.0.80","0.0.81","0.0.82","0.0.83","0.0.84","0.0.85","0.0.86","0.0.87","0.0.88","0.0.89","0.0.9","0.0.90","0.0.91","0.0.92","0.0.93","0.0.94","0.0.95","0.0.96","0.0.97","0.0.98","0.0.99","0.1.0","0.1.1","0.1.10","0.1.11","0.1.12","0.1.13","0.1.14","0.1.15","0.1.16","0.1.17","0.1.18","0.1.19","0.1.2","0.1.20","0.1.21","0.1.22","0.1.23","0.1.24","0.1.25","0.1.26","0.1.27","0.1.3","0.1.4","0.1.5","0.1.6","0.1.7","0.1.8","0.1.9","0.10.0","0.10.1","0.10.10","0.10.2","0.10.3","0.10.4","0.10.5","0.10.6","0.10.7","0.10.8","0.10.9","0.11.0","0.11.1","0.11.10","0.11.11","0.11.12","0.11.13","0.11.14","0.11.15","0.11.16","0.11.17","0.11.18","0.11.19","0.11.2","0.11.20","0.11.21","0.11.22","0.11.23","0.11.24","0.11.25","0.11.27","0.11.28","0.11.29","0.11.3","0.11.30","0.11.31","0.11.4","0.11.5","0.11.6","0.11.7","0.11.8","0.11.9","0.12.0","0.12.1","0.12.10","0.12.11","0.12.12","0.12.13","0.12.14","0.12.15","0.12.16","0.12.17","0.12.18","0.12.19","0.12.2","0.12.20","0.12.21","0.12.3","0.12.4","0.12.5","0.12.6","0.12.7","0.12.8","0.12.9","0.13.0","0.13.1","0.13.10","0.13.11","0.13.12","0.13.13","0.13.14","0.13.15","0.13.16","0.13.17","0.13.18","0.13.19","0.13.2","0.13.20","0.13.21","0.13.22","0.13.23","0.13.3","0.13.4","0.13.5","0.13.6","0.13.7","0.13.8","0.13.9","0.14.0","0.14.1","0.14.10","0.14.11","0.14.12","0.14.14","0.14.15","0.14.16","0.14.2","0.14.3","0.14.4","0.14.5","0.14.6","0.14.7","0.14.8","0.14.9","0.15.0","0.15.1","0.15.2","0.15.3","0.2.0","0.2.1","0.2.2","0.3.0","0.3.1","0.3.2","0.3.3","0.3.4","0.4.0","0.4.1","0.5.0","0.5.1","0.5.2","0.5.3","0.6.0","0.6.1","0.6.2","0.6.3","0.6.4","0.6.5","0.6.6","0.6.7","0.6.8","0.7.0","0.7.1","0.8.0","0.8.1","0.9.0","0.9.1","1.0.0","1.1.0","1.2.0","1.2.1","1.2.2","1.2.3","1.2.4","1.3.0","1.3.1","1.4.0","1.4.1","1.4.2","1.4.3","1.4.4","1.4.5","1.4.6","1.4.7","1.4.8","1.5.0","1.5.1","1.5.10","1.5.100","1.5.101","1.5.102","1.5.103","1.5.104","1.5.105","1.5.106","1.5.107","1.5.108","1.5.109","1.5.11","1.5.110","1.5.111","1.5.112","1.5.113","1.5.114","1.5.115","1.5.116","1.5.117","1.5.118","1.5.119","1.5.12","1.5.120","1.5.121","1.5.122","1.5.123","1.5.124","1.5.125","1.5.126","1.5.127","1.5.128","1.5.129","1.5.13","1.5.130","1.5.131","1.5.132","1.5.133","1.5.134","1.5.135","1.5.136","1.5.137","1.5.138","1.5.139","1.5.14","1.5.140","1.5.141","1.5.142","1.5.143","1.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