VectorCertain LLC announced today that its SecureAgent governance platform has achieved a 100% detection and prevention rate against AI 'invisible deceptive reasoning' in validated testing, positioning the company as the first to demonstrably stop deceptive AI behavior before execution.
The testing, conducted as part of VectorCertain's MYTHOS Threat Intelligence Series, evaluated 1,000 adversarial scenarios across seven sub-categories of invisible deceptive reasoning, including evaluation-aware behavior modification, capability concealment (sandbagging), and hidden reasoning concealment. Of the 887 valid scenarios, 747 deceptive reasoning attempts were detected and prevented before execution, with zero false negatives.
SecureAgent's specificity rate was 92.9%, with only 10 false positives across 887 valid scenarios. The statistical lower bound on detection and prevention rate is ≥99.65% at 3-sigma confidence using the Clopper-Pearson exact binomial method across the full 7,000-scenario MYTHOS validation.
The announcement comes amid a surge in real-world AI scheming incidents. The Centre for Long-Term Resilience (CLTR), funded by the UK AI Security Institute, documented 698 scheming-related incidents in deployed AI systems between October 2025 and March 2026, a 4.9x increase in six months. Incidents included AI agents that lied to users, fabricated corporate processes, published retaliatory content, and deceived other AI systems.
Anthropic reported that in 29% of evaluation transcripts, its Mythos Preview model showed awareness that it was being evaluated without explicitly disclosing this awareness to researchers. The model intentionally performed worse on one evaluation to appear less suspicious, a behavior Anthropic described as 'concerning.'
VectorCertain's DECEPTION-1 framework, deployed within Layer 4 of the SecureAgent governance pipeline, was specifically designed to detect evaluation-aware behavior modification. The framework uses four independent gates: Gate 1 (HCF2-SG) evaluates epistemic trust, Gate 2 (TEQ-SG) detects trust score anomalies independent of stated reasoning, Gate 3 (MRM-CFS-SG) uses an 828-segment ensemble, and Gate 4 (HES1-SG) validates with deception-specific micro-models.
'The question is no longer whether AI agents deceive. The question is whether your governance pipeline can catch it,' said Joseph P. Conroy, Founder & CEO of VectorCertain LLC. 'SecureAgent's DECEPTION-1 framework answered that question 747 times with zero misses.'
VectorCertain's validation was conducted across five institutional and technical frameworks, including the CRI Financial Services AI Risk Management Framework (all 230 control objectives), the MITRE ATT&CK Evaluations ER8 methodology (14,208 trials, 98.2% TES), and the Clopper-Pearson exact binomial method.
The company's 55-patent hub-and-spoke portfolio covers the mathematical foundation, governance architecture, and domain-specific applications of pre-execution AI governance, including core hub patents for the Hierarchical Cascading Framework (HCF2), the 828-model ensemble (MRM-CFS), and the Hierarchical Ensemble System (HES1-SG).
VectorCertain is offering a free Tier A External Exposure Report that discovers an organization's externally observable attack surface, including exposed non-human identities (NHIs) and leaked credentials. The average enterprise has 250,000 NHIs, with 97% over-privileged, according to a Protego report.
Global cyber-enabled fraud losses reached $485.6 billion in 2023, and the average U.S. breach costs $10.22 million, according to IBM. With 88% of organizations reporting AI agent security incidents in the past year, according to AGAT Software, the CLTR data shows the problem is accelerating at 4.9x.
