Core principle

Safety & guardrails

UCogNet's self-improvement is not unconstrained. Every mutation, every execution, and every deployment is bounded by statistical gates, budget limits, anomaly detection, and automatic rollback. This page documents the safety architecture in detail.

Adversarial evidence · prompt injection

We red-teamed our own safety gate — and it made us re-engineer it.

Claims are cheap. We ran a sovereign, AgentDojo-style prompt-injection benchmark against our own agent — no third-party API, nothing leaves the machine — and measured what our gates actually do. The result below is cryptographically signed; verify it in your browser.

Signed result — verified in your browser
Bare model
Gate · uncalibrated
Gate · calibrated
Prompt-injection success rate
17%
0%
0%
Legitimate action completed
100%
0%
100%
AgentDojo-style methodology (the “important_instructions” attack). The bare attack rate varies run-to-run even at temperature 0; the robust result is the utility recovery without losing the injection block.
The finding
A perfect but blunt gate

Our Ananke irreversibility gate stopped 100% of injected actions — but it refused every irreversible action, blocking legitimate ones too. Not deployable as-is. We reported it instead of hiding it.

Why we changed it
Irreversibility ≠ harm

An irreversible action the user asked for is legitimate; one requested by untrusted content is the attack. We calibrated Ananke to condition on provenance — fail-closed by default, relaxed only for attested, user-originated actions an attacker cannot fake.

Re-measured
Protection kept, utility restored

The calibrated gate still blocks attempted injections and holds attack success at 0% — while legitimate actions now complete again (0% → 100%).

Verify the signatures yourselfed25519-signed with the same key as every U-CogNet decision · sovereign benchmark · no OpenAI.

Safety pillars

Six interlocking mechanisms that prevent uncontrolled self-improvement.

Gated evolution

Every policy mutation must pass A/B statistical gates with bootstrap confidence intervals before deployment. Mutations deploy gradually: 10% → 30% → 100% traffic.

Anomaly detection

Reward spikes greater than 3σ from rolling mean trigger automatic audit and halt. Prevents reward hacking and distribution shift exploitation.

Cost caps & budgets

Every execution operates under token, time, cost, and tool call budgets. Overruns trigger immediate rollback — no runaway inference.

Sandboxed execution

All tool calls execute in isolated sandboxes with strict permissions. No ambient authority — tools declare required capabilities upfront.

Evidence auditing

Every response carries structured claims with provenance. Outputs without evidence are flagged and cannot be trusted by downstream consumers.

Automatic rollback

If any gate fails at any deployment stage, the system reverts to the previous policy within one evaluation cycle. No human intervention required.

Shaping guardrails

Anti-reward-hacking mechanisms at the reward layer.

Shaping cap and anomaly detection

Capability boundaries

What the system can and cannot do — honestly mapped.

Capability boundaries honesty map

A/B gate protocol

Every candidate policy must clear every gate in sequence. One failure triggers full rollback.

01

Improvement threshold

Candidate must exceed baseline by a statistically significant margin (bootstrap CI).

02

Cost constraint

New mutation cannot exceed 1.2× the cost of current best policy.

03

Safety anomaly check

Reward spikes > 3σ from rolling mean trigger automatic audit and halt.

04

Gradual rollout

10% → 30% → 100% traffic with gates at each stage.

05

Rollback guarantee

If any gate fails, system reverts to previous policy within one evaluation cycle.

Evidence asymmetry analysis

Evidence asymmetry

Reward composition breakdown

Reward composition

Want to audit our safety architecture?

Full safety documentation is available under NDA for qualified partners and investors.