Models may handle simple questions well, but they break down more often when nuance, judgment, or context is required creating higher risk. This benchmark helps leaders track safety, compare vendors, and strengthen AI governance before deploying in high impact use cases.
To ensure AI is built and deployed with integrity, accountability, and transparency
22%
of attacks succeed on frontier models
>1
semantic leak in a million still unsafe
>25%
bias persists across models
our code
Scale your AI ambition, Omni Reach’s approach
Our AI Code of Ethics is a guiding framework that informs every AI solution we build.
Fairness
Auditing data and models to prevent discriminatory outcomes.
Accountability
Clear ownership and traceability of AI decisions, actions, and outcomes.
Data Governance
Ensuring data quality, access control, privacy, and lifecycle management across the AI pipeline.
Modular
To evolve freely without the constraints of legacy systems.
Explainability
AI that guarantees that decisions can be understood and justified
Risk Management
Proactively identifying, measuring, and reducing AI risks before they impact customers or compliance.
Sustainability
Efficient computing, responsible data storage, and mindful energy use
Human in Loop
Keeping humans in control for review, override, and approval of critical AI decisions.
22% Attacks succeed on frontier models
Automated red teaming can bypass AI guardrails and pull restricted, dangerous info with high success using simple natural language prompts. Because low cost tools can do this across many AI systems, the risk is scalable and systemic.
Transluce | Published: September 3, 2025
>1 semantic leak in a million Still Unsafe
Semantic leakage occurs across multiple models and languages, making outputs less reliable and potentially amplifying hidden biases or manipulation risks. The key takeaway is that prompt level controls aren’t enough models need stronger safeguards to prevent subtle, unintended influence.
Semantic Leaks LLM | Published in NAACL 2025
>25% bias persists across models
