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18/08/2026 2:34 pm
TL;DR: GPT-4.5 reduces hallucinations and improves privacy resilience, according to VirtueAI’s red-teaming analysis, while Claude 3.7 performs better on regulatory compliance, adversarial prompt resistance, and blocking malicious code generation. The practical issue is no longer model quality alone, but how organisations govern misuse, refusal behaviour, and multi-modal attack surfaces across AI deployments.
NHIMG editorial — based on content published by VirtueAI: GPT-4.5 vs Claude 3.7 – Advanced Redteaming Analysis
Questions worth separating out
Q: How should organisations test frontier models before production use?
A: They should test frontier models against the actual risks their workflows create, not just benchmark scores.
Q: Why do AI governance programmes need separate tests for code and privacy risk?
A: Because a model can fail in one domain while appearing strong in another.
Q: What do security teams get wrong about AI safety testing?
A: The common mistake is treating AI safety testing as if it were just another security scan.
Practitioner guidance
- Separate safety tests by risk domain Run distinct evaluation suites for hallucination, privacy leakage, code generation, refusal behaviour, and multi-modal prompt injection so one passing score does not hide another failure mode.
- Gate tool access behind model-specific approvals Do not let every model connect to the same tools, files, or APIs.
- Test generated code as untrusted output Treat model-produced scripts, snippets, and configuration as untrusted until they pass secure review, static analysis, and change-control checks.
What's in the full article
VirtueAI's full blog post covers the operational detail this post intentionally leaves for the source:
- Test-by-test observations across safety, hallucination, privacy, and compliance scenarios for both models
- Examples of harmful or rejected outputs used in the red-teaming process
- Expanded discussion of VirtueRed's approach to model and application assessment
- The vendor's own interpretation of where each model performs better or worse across specific misuse patterns
👉 Read VirtueAI's analysis of GPT-4.5 vs Claude 3.7 red-teaming results →
GPT-4.5 vs Claude 3.7: what red-teaming says about AI risk?
Explore further
18/08/2026 3:20 pm
Model red-teaming is becoming a governance control, not a lab exercise. The article shows that safety, compliance, privacy, and code-generation weaknesses can diverge sharply between two frontier models. That means AI risk management has to be evidence-led and scenario-specific rather than based on brand reputation or generic benchmark performance. For AI governance teams, red-teaming results should feed approval, monitoring, and escalation decisions before deployment.
A question worth separating out:
Q: How should teams govern AI-assisted coding when the model writes most of the implementation?
A: Teams should govern the specification first, then the code. The PRD should define architecture, constraints, tests, and acceptance criteria before generation begins, because that is where ambiguity is controlled. Once work starts, the document must stay current so reviewers can validate decisions, not just inspect output after the fact.
👉 Read our full editorial: GPT-4.5 vs Claude 3.7 reveals model red-teaming trade-offs
