A Safety Monitor That Admits It Can Miss
NeuralShield-AI is my attempt to treat an LLM the way network engineers treat a perimeter: more than one control, each one assuming the one before it will sometimes fail. The README describes it as a defense framework for threats aimed at AI systems, including prompt injection, jailbreak attempts, hallucinations, data poisoning and adversarial inputs.
Seven layers, one boundary
The layers are listed in the README in order. Layer 1 validates and sanitizes input at the boundary. Layer 2 filters content for toxicity, harm and policy violations. Layer 3 watches model output for behavioral anomalies and hallucinations. Layer 4 checks context and data integrity, including poisoning. Layer 5 covers threat intelligence and indicators of compromise. Layer 6 automates response, with quarantine and playbooks. Layer 7 handles audit, forensics and compliance, including decision replay and MITRE ATT&CK mapping.
The quick start shows the shape of it. You create a shield, add layers such as a prompt injection detector with a threshold of 0.85 and a hallucination monitor with a confidence threshold of 0.7, scan the prompt before it reaches the model, then scan the output afterwards. If a threat is detected, the result carries a type, a severity and a mapped technique, and the request can be blocked or quarantined.
Why the disclaimer comes first
The README puts a disclaimer above the architecture: NeuralShield-AI is a research-grade tool, AI safety is an unsolved problem, and no monitoring system can guarantee 100 percent detection. Its safety section goes further. It lists a false sense of security, adversaries who adapt, false positives and negatives, thresholds that are tuned to one model and may not transfer, privacy risk from logging prompts and outputs, bias in ML detectors against some dialects, and the fact that the monitor is itself software that could be compromised.
A security tool that promises certainty trains its users to stop thinking. The README says to keep a human in the loop for high-severity incidents and to treat monitoring as a supplement to alignment work and red-teaming, not a replacement.
How it lines up with the public threat list
OWASP's Top 10 for LLM Applications 2025 puts prompt injection first, as LLM01. Its description is that user prompts alter the model's behavior or output in unintended ways, and that the inputs do not need to be human-readable as long as the model parses them. That is why Layer 1 sits at the boundary and why the README also lists indirect injection in retrieval contexts and tool outputs as something to detect.
What I would not claim
The README describes hundreds of modules and a continuous-learning pipeline. I have not published detection rates against a public benchmark, so I will not quote one here. If you use the project, run it against your own model and your own traffic, and tune the thresholds there.
Sources
NeuralShield-AI README: github.com/yethikrishna/NeuralShield-AI. OWASP Gen AI Security Project, LLM01:2025 Prompt Injection: genai.owasp.org/llmrisk/llm01-prompt-injection/.