CVE-2026-2970: Datapizza-ai Deserialization Vulnerability

CVE-2026-2970 Overview

A deserialization vulnerability has been identified in datapizza-labs datapizza-ai version 0.0.2. The vulnerability exists in the RedisCache function within the file datapizza-ai-cache/redis/datapizza/cache/redis/cache.py. This insecure deserialization flaw could allow an attacker with access to the local network to manipulate serialized data, potentially leading to unauthorized code execution or data manipulation.

Critical Impact

Attackers on the local network could exploit unsafe deserialization in the Redis caching mechanism to potentially execute arbitrary code or compromise cached data integrity.

Affected Products

• datapizza datapizza-ai version 0.0.2

Discovery Timeline

• 2026-02-23 - CVE CVE-2026-2970 published to NVD
• 2026-02-25 - Last updated in NVD database

Technical Details for CVE-2026-2970

Vulnerability Analysis

This vulnerability stems from insecure deserialization practices in the Redis caching component of the datapizza-ai library. When the application deserializes data retrieved from Redis without proper validation, an attacker positioned on the adjacent network can craft malicious serialized objects that execute arbitrary code upon deserialization.

The vulnerability requires the attacker to have access to the local network where the Redis cache is accessible. While the attack complexity is high and exploitation appears difficult, the public disclosure of this vulnerability increases the risk of targeted attacks against vulnerable installations.

Python's pickle module, commonly used for serialization in Redis caching implementations, is particularly dangerous when deserializing untrusted data, as it can instantiate arbitrary Python objects and execute code during the unpickling process.

Root Cause

The root cause is the use of unsafe deserialization methods in the RedisCache function located in datapizza-ai-cache/redis/datapizza/cache/redis/cache.py. The application appears to deserialize cached data from Redis without implementing proper input validation or using a safer serialization format. This allows maliciously crafted serialized payloads to be processed, potentially leading to code execution.

Attack Vector

The attack vector requires adjacent network access, meaning the attacker must be positioned on the same local network segment as the target system. The attack flow involves:

1. The attacker gains access to the local network where the Redis instance is accessible
2. The attacker crafts a malicious serialized payload designed to execute code upon deserialization
3. The payload is injected into the Redis cache
4. When the vulnerable application retrieves and deserializes the cached data, the malicious code executes

The vulnerability manifests in the RedisCache function where cached data is deserialized. For technical details and proof-of-concept information, refer to the GitHub Unsafe Deserialization Disclosure.

Detection Methods for CVE-2026-2970

Indicators of Compromise

• Unusual Python processes spawned by applications using the datapizza-ai library
• Unexpected outbound network connections from the Redis cache host
• Suspicious entries in Redis cache containing encoded or obfuscated data patterns typical of pickle exploitation payloads
• Anomalous deserialization errors in application logs

Detection Strategies

• Monitor Redis commands for suspicious SET operations containing serialized Python objects with unexpected class references
• Implement network segmentation monitoring to detect unauthorized access to Redis instances from adjacent network segments
• Deploy application-level logging to track deserialization operations in the cache.py module
• Use SentinelOne's behavioral detection to identify malicious code execution patterns following deserialization events

Monitoring Recommendations

• Enable verbose logging on Redis instances to capture all incoming commands and their sources
• Implement file integrity monitoring on the datapizza-ai-cache/redis/datapizza/cache/redis/cache.py file
• Monitor process creation events for applications using datapizza-ai to detect exploitation attempts
• Configure network monitoring to alert on unexpected traffic patterns to and from Redis ports

How to Mitigate CVE-2026-2970

Immediate Actions Required

• Restrict network access to Redis instances using firewall rules and network segmentation
• Audit all systems using datapizza-ai version 0.0.2 and assess exposure
• Consider temporarily disabling Redis caching functionality until a patch is available
• Enable Redis authentication and TLS encryption to add additional security layers

Patch Information

No official patch is currently available from the vendor. According to the vulnerability disclosure, the vendor was contacted early about this issue but did not respond. Organizations should monitor the datapizza-ai repository for updates or consider alternative caching solutions.

Workarounds

• Implement network-level access controls to restrict Redis access to only trusted hosts
• Deploy a Redis proxy that validates and sanitizes data before it reaches the application
• Consider replacing pickle-based serialization with safer alternatives like JSON where possible
• Isolate systems running datapizza-ai in a separate network segment with strict egress controls
• Monitor the VulDB entry for updated mitigation guidance

# Example: Restrict Redis access using iptables
# Only allow connections from trusted application servers
iptables -A INPUT -p tcp --dport 6379 -s 192.168.1.0/24 -j ACCEPT
iptables -A INPUT -p tcp --dport 6379 -j DROP

# Enable Redis authentication in redis.conf
# requirepass your_strong_password_here