CVE-2026-54769: Langroid TableChatAgent Sandbox Escape via eval() RCE (CVSS 10.0)

Vulnerability Overview

CVE-2026-54769 is a critical code injection vulnerability (CWE-94) in Langroid's TableChatAgent component. The root cause is an incomplete mitigation of Python's eval() function — the sandbox intended to restrict arbitrary code execution fails to account for crafted inputs that traverse the restriction boundary. An unauthenticated attacker who can supply input to a TableChatAgent instance can escape the sandbox entirely and achieve remote code execution (RCE) on the underlying host system. The vulnerability carries a CVSS score of 10.0, reflecting the combination of network accessibility, no required privileges, no user interaction, and full impact across confidentiality, integrity, and availability.

Affected Software

The vulnerability affects the langroid Python package (distributed via pip), versions <= 0.65.1. Any application or service that instantiates TableChatAgent and exposes it to untrusted input — including LLM pipelines, agentic frameworks, and data analysis assistants built on Langroid — should be treated as directly at risk. At time of writing, no patched version has been published.

Exploitation Status

A public proof-of-concept (PoC) exploit is available. This significantly lowers the barrier to exploitation: opportunistic attackers do not need to independently reverse-engineer the bypass. Given that no patch exists and the affected component is commonly used in AI/LLM application stacks, defenders should treat active exploitation attempts as a realistic near-term threat. Any internet-facing service backed by Langroid TableChatAgent is a high-priority remediation target.

What This Means for Defenders

• Immediate: audit all deployments for Langroid versions <= 0.65.1 and assess exposure surface.
• Short-term: implement input validation layers at the application boundary to reject inputs containing known sandbox-escape patterns.
• Monitoring: treat unexpected process spawning, outbound connections, or file writes originating from your Langroid service process as high-confidence indicators of compromise.

Detection Coverage

Our detection logic targets behavioral indicators that are difficult to suppress even when the exploit payload is obfuscated: anomalous child process creation from Python interpreter processes running Langroid, unexpected eval()-triggered syscall chains, and network or filesystem activity inconsistent with normal TableChatAgent operation.

We ship fully tested, production-ready queries across seven SIEM platforms:

• Microsoft Sentinel (KQL) — correlates process lineage and command-line arguments against known Langroid execution patterns.
• Splunk (SPL) — hunts for anomalous subprocess execution and outbound connections from the Python runtime.
• Elastic (EQL) — sequence-based detection pairing the initial eval invocation with downstream process or network events.
• IBM QRadar (AQL) — flow and event correlation targeting the escape chain.
• Sumo Logic — log-based detection covering both process and network telemetry.
• Chronicle (YARA-L) — rule-based detection against UDM events for the full exploitation sequence.
• CrowdStrike (CQL) — endpoint-sourced detection leveraging process tree visibility.

Each query is tuned to minimize false positives in environments where Python and pip-installed packages are standard tooling, focusing on the specific behavioral fingerprint of a successful sandbox escape rather than broad eval() usage.

Full Detection & Queries

The complete detection page — including all SIEM queries, MITRE ATT&CK mapping, and triage guidance — is available at /detections/CVE-2026-54769. Paid subscribers receive the full playbook and atomic test cases for validating detection coverage in your environment.