CVE-2025-55556
📋 TL;DR
TensorFlow v2.18.0 has a bug where Embedding layers produce random outputs during compilation instead of expected results, causing ML models to generate incorrect predictions. This affects applications using TensorFlow for inference or training with Embedding layers. The vulnerability impacts all users of TensorFlow v2.18.0 who compile models with Embedding components.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Critical ML applications (like medical diagnosis, autonomous systems, or fraud detection) produce completely unreliable outputs, leading to safety incidents, financial losses, or operational failures.
Likely Case
ML models produce inconsistent or degraded performance, causing incorrect predictions, reduced accuracy, and potential business logic errors in applications relying on these models.
If Mitigated
With proper testing and validation pipelines, the issue would be caught during development or QA before reaching production, minimizing operational impact.
🎯 Exploit Status
Exploitation requires the attacker to trigger model compilation with Embedding layers, which typically happens during normal application usage rather than being directly attackable.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow v2.18.1 or later
Vendor Advisory: https://github.com/tensorflow/tensorflow/issues/82317
Restart Required: No
Instructions:
1. Update TensorFlow using pip: 'pip install --upgrade tensorflow==2.18.1' 2. Verify the update with 'pip show tensorflow' 3. Recompile any affected models to ensure they use the fixed version.
🔧 Temporary Workarounds
Downgrade to previous stable version
allTemporarily revert to TensorFlow v2.17.0 which does not have this bug
pip install tensorflow==2.17.0
Avoid Embedding compilation in v2.18.0
allIf possible, delay compiling models with Embedding layers until patched
🧯 If You Can't Patch
- Implement rigorous output validation for models using Embedding layers to detect anomalous results
- Isolate affected TensorFlow services and implement circuit breakers to prevent cascading failures
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version with 'python -c "import tensorflow as tf; print(tf.__version__)"' - if output is '2.18.0', the system is vulnerable.Check Version:
python -c "import tensorflow as tf; print(tf.__version__)"Verify Fix Applied:
After updating, run the same command and ensure version is '2.18.1' or higher. Test compilation of Embedding layers to verify they produce deterministic outputs.📡 Detection & Monitoring
Log Indicators:
- Unexpected model output patterns
- Increased error rates in ML inference services
- Anomalous prediction distributions
Network Indicators:
- Increased API error responses from ML services
- Unusual retry patterns from clients
SIEM Query:
source="tensorflow" AND (message="Embedding" OR message="compilation") AND error_level="ERROR"