badBANANA Research / drift_orchestrator

The evaluator is part of the attack surface.

A public evidence package for studying policy drift, evaluator manipulation, and failure modes in a tested LLM safety-monitor architecture.

SCOPE: recorded experiments cover six injection vectors across qwen2.5:3b, mistral, and phi3:mini in the documented local environment. Results from that set are not claims of universal behavior across arbitrary models, versions, prompts, or safety architectures.

Recorded findings

The useful claim is not “every evaluator fails.” It is that the tested architecture exposed repeatable failure modes worth measuring, reproducing, and designing around.

01 / SECOND-ORDER INJECTION

Evaluator input can become evaluator control.

In the tested setup, attacker-influenced session content could alter the LLM evaluator's verdict rather than merely being classified by it.

Evidence: committed phase-1 and phase-2 JSONL outputs plus probe scripts.
02 / V4 REASONING CAPTURE

100% bypass in the tested three-model set.

V4 produced 100% bypass in the recorded qwen2.5:3b, mistral, and phi3:mini runs and transferred without per-model modification among those tested models.

Boundary: transfer within the tested set; not a universal exploit claim.
03 / COUPLED EVALUATORS

Symmetric injection collapsed the expected divergence signal.

When both tested evaluators received the same manipulation, recorded inter-system divergence fell to approximately 0.01, removing the redundancy signal the governor expected to use.

Evidence: coupled-system experiment outputs; architecture-specific result.
04 / NEGATIVE RESULT

Calibration reduced false alarms and detection together.

Baseline calibration improved the uncalibrated meta-evaluator false-alarm rate but also reduced V4 detection in the corresponding recorded experiment.

Useful because the defense tradeoff is preserved rather than hidden.

Vector results

Headline values below summarize the committed experiment set. Review raw denominators, skipped/error responses, model identifiers, and probe logic before comparing a fresh run.

Vectorqwen2.5:3bmistralphi3:miniObserved transfer
V1 — direct instruction80–100%100%33%partial
V2 — role confusion80%100%39%partial
V3 — format exploit40%100%variednot established
V4 — reasoning capture100%100%100%yes, tested set
V5 — authority escalation~80%100%17%not established
V6 — format mimicry100%100%89%yes, tested set

Failure chain

This describes the recorded architecture under test. It is a mechanism-oriented explanation, not a claim that every safety monitor has the same control flow.

01

Incremental policy drift

A semantic-gradient sequence creates small per-step movement while producing larger cumulative policy change.

02

Evaluator-facing manipulation

The tested V4 pattern is included in content consumed by the LLM evaluator.

03

Geometric signal remains below threshold

Incremental movement can avoid the configured abrupt-drift trigger in the recorded sequence.

04

Semantic verdict is altered

The evaluator returns the manipulated verdict in the vulnerable recorded runs.

05

Coupled divergence collapses

Symmetric manipulation causes both tested evaluators to agree, reducing the expected disagreement signal.

06

Governor loses both expected alarms

The experiment completes without the tested geometric or evaluator-divergence controls producing the intended veto.

Evidence boundary

The repository is strongest when a reader can tell exactly what the experiments establish and what remains an open question.

Established in the recorded setSpecific probe/model/configuration outcomes, committed outputs, tested transfer among the three reference model families, and architecture-specific failure behavior.
Not establishedUniversal model vulnerability, universal payload portability, production-system exploitability, vendor-wide behavior, or effectiveness against arbitrary safety architectures.
Fresh rerunsNow publicly possible through the included Ollama-compatible gateway adapter. New outputs can differ because model/runtime/hardware and nondeterministic inference can drift.