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Secure & Audit

Prompt injection defense, adversarial red-teaming, bias auditing, model watermarking, and output guardrails.

The problem

LLMs face jailbreaks, data leaks, and fairness gaps. Shipping without systematic testing is a compliance and security liability.

  • The first adversarial input a model sees in production is also the first one anyone tested.
  • A prompt injection attempt succeeds and you find out from a screenshot, not your own logs.
  • "It seemed robust" isn't something you can put in a release checklist.

How NEO helps

NEO builds defense layers, red-team style probes, and audit-friendly reports so you can harden prompts, outputs, and models before release.

  • Builds multi-layer prompt injection defenses scored in real time, under 200ms
  • Stress-tests models against multiple adversarial attack types and grades robustness A–F

Scenarios (2)

Prompt Injection Defense
98.9% detection accuracy

Secure & Audit

Prompt Injection Defense

Multi-layer defense system detecting adversarial LLM attacks with 98.9% accuracy at under 200ms latency.

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Adversarial Robustness Probe
7 attack types, A–F grading

Secure & Audit

Adversarial Robustness Probe

Seven attack types against NLP and vision models, measuring prediction flip rates with shareable HTML reports.

Read case study

Run this workflow in your workspace

Install NEO in VS Code or Cursor and ship training, evals, and deployment from chat.

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eval_pipeline.py
  def run_eval(model, tasks):-     results = model.predict(tasks)+     results = batch_eval(model, tasks)+     log_regression(results)      return score(results)