CVE-2024-6507: Kaggle Dataset Ingestion RCE Vulnerability

CVE-2024-6507 Overview

CVE-2024-6507 is a command injection vulnerability in the Activeloop Deep Lake machine learning data lake library. The flaw resides in the ingest_kaggle() API, which fails to sanitize user-supplied input before passing it to a shell command that retrieves remote Kaggle datasets. An attacker who controls the dataset identifier or related parameters can inject arbitrary operating system commands. Successful exploitation results in arbitrary command execution on the host running Deep Lake. The issue is tracked under [CWE-78] OS Command Injection and [CWE-94] Improper Control of Code Generation.

Critical Impact
Arbitrary OS command execution on systems ingesting attacker-influenced Kaggle dataset identifiers through Deep Lake.

Affected Products

Activeloop Deep Lake (deeplake) Python package
Applications using the ingest_kaggle() API to load remote Kaggle datasets
ML and data pipelines that pass untrusted strings to Deep Lake ingestion functions

Discovery Timeline

2024-07-04 - CVE-2024-6507 published to NVD
2026-04-15 - Last updated in NVD database

Technical Details for CVE-2024-6507

Vulnerability Analysis

Deep Lake exposes the ingest_kaggle() helper to download and convert Kaggle datasets into Deep Lake format. Internally, the function builds a shell command string using user-supplied parameters such as the Kaggle dataset path, then executes that string through the operating system shell. Because the function does not validate or escape shell metacharacters, an attacker who controls any of these parameters can append additional commands using characters such as ;, &&, |, or backticks. The injected commands run with the privileges of the process invoking Deep Lake, which is often a data science workstation or training server with broad access to credentials, datasets, and cloud tokens.

Root Cause

The root cause is missing input sanitization combined with shell-based command construction. The ingest_kaggle() implementation concatenates untrusted strings into a command that is executed through a shell interpreter rather than passed as an argument vector to a process API. This pattern allows shell metacharacters in the dataset identifier to break out of the intended command structure. The fix introduced in the upstream GitHub pull request restricts and validates the parameters before invocation.

Attack Vector

The attack vector is network-based but requires that an application accept attacker-controlled values for the Kaggle dataset parameters passed to ingest_kaggle(). Exploitation scenarios include multi-tenant ML platforms that accept user-supplied dataset names, notebooks that fetch dataset identifiers from external sources, and automated pipelines triggered by webhook payloads. Attack complexity is elevated because exploitation depends on how the host application exposes the API. Refer to the JFrog Vulnerability Report for further technical analysis.

Detection Methods for CVE-2024-6507

Indicators of Compromise

Unexpected child processes spawned by Python interpreters running deeplake workloads, such as sh, bash, curl, wget, or nc.
Shell metacharacters (;, &&, |, backticks, $()) appearing in logged Kaggle dataset identifiers or API parameters.
Outbound connections from ML training hosts to domains unrelated to Kaggle or configured dataset registries.
Modifications to credential files such as ~/.kaggle/kaggle.json, ~/.aws/credentials, or SSH keys following dataset ingestion jobs.

Detection Strategies

Inspect application logs and request bodies for shell metacharacters in fields forwarded to ingest_kaggle().
Apply EDR rules that flag Python processes spawning shell utilities or networking binaries on data science hosts.
Perform static analysis of code repositories to locate direct calls to ingest_kaggle() that receive external input.

Monitoring Recommendations

Forward process-creation telemetry from ML training nodes to a centralized data lake for correlation against ingestion jobs.
Monitor egress traffic from training environments and alert on connections that bypass approved dataset domains.
Track installed deeplake versions across notebooks, containers, and CI runners to identify unpatched instances.

How to Mitigate CVE-2024-6507

Immediate Actions Required

Upgrade the deeplake package to the version that contains the fix from pull request #2876.
Audit all application code paths that invoke ingest_kaggle() and confirm that dataset parameters are not derived from untrusted input.
Rotate any Kaggle, cloud provider, or registry credentials stored on hosts that may have executed attacker-controlled ingestion calls.

Patch Information

The vulnerability is addressed in the upstream commit merged through activeloopai/deeplake PR #2876. Update deeplake through pip to the fixed release and rebuild any container images or pinned dependency manifests that reference the vulnerable version.

Workarounds

Restrict ingest_kaggle() callers to trusted operators and reject any externally supplied dataset identifiers until patching is complete.
Enforce an allowlist of Kaggle dataset paths matching a strict pattern such as ^[A-Za-z0-9_\-]+/[A-Za-z0-9_\-]+$.
Run Deep Lake ingestion jobs inside sandboxed containers with no outbound network access except to required dataset endpoints, and with non-privileged user accounts.

bash

# Configuration example: upgrade deeplake and validate the Kaggle dataset identifier
pip install --upgrade deeplake

# Reject input containing shell metacharacters before calling ingest_kaggle()
DATASET="$1"
if ! [[ "$DATASET" =~ ^[A-Za-z0-9_\-]+/[A-Za-z0-9_\-]+$ ]]; then
  echo "Invalid Kaggle dataset identifier" >&2
  exit 1
fi
python -c "import deeplake; deeplake.ingest_kaggle(tag='$DATASET', src='./local', dest='./ds')"