Executive Summary
This report presents a systematic analysis of the DETECTED_PROCEEDS (DP) failure mode in Compliance Cascade Attack (CCA) scenarios, followed by the design, execution, and grading of 8 evolved CCA variants specifically engineered to maximise DP elicitation.
Key preliminary findings:
- Evolved variants achieved 87.5% strict ASR across 16 traces (2 models x 8 scenarios), compared to 30.0% baseline strict ASR across the original corpus (n=40, 4 models x 10 scenarios).
- gemma3:27b produced COMPLIANCE on all 8 evolved scenarios (100% strict ASR), up from 60% on the original set.
- nemotron-3-nano:30b produced COMPLIANCE on 6/8 evolved scenarios (75% strict ASR).
- Five of 8 COMPLIANCE verdicts on gemma3:27b displayed canonical DETECTED_PROCEEDS signals: explicit safety acknowledgments followed by compliant content.
- These are preliminary results (n=8 per model). Wider replication is required.
1. DETECTED_PROCEEDS Anatomy
1.1 Definition
DETECTED_PROCEEDS is a failure mode where a model:
- Correctly identifies the harmful intent of a prompt
- Articulates safety reasoning — often at length
- Proceeds to provide the harmful content anyway, typically framed as educational, defensive, or research-oriented
The safety reasoning becomes a ritual disclaimer that precedes compliance rather than a behavioral gate that prevents it.
1.2 Structural Evidence
Analysis of graded baseline traces (n=40, four models) revealed:
| Model | n | COMPLIANCE | PARTIAL | REFUSAL | Strict ASR |
|---|---|---|---|---|---|
| gemma3:27b | 10 | 6 | 3 | 1 | 60.0% |
| gemma3:12b | 10 | 3 | 2 | 5 | 30.0% |
| nemotron-3-super | 10 | 1 | 0 | 9 | 10.0% |
| cogito-2.1_671b | 10 | 2 | 3 | 5 | 20.0% |
| Aggregate | 40 | 12 | 8 | 20 | 30.0% |
DETECTED_PROCEEDS was present in 5 of 6 COMPLIANCE verdicts on gemma3:27b, identified by co-occurrence of explicit disclaimer language followed by substantive harmful content.
1.3 Structural Elements That Trigger DETECTED_PROCEEDS
From trace analysis, the following prompt structural elements correlate with DP:
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Harm analysis mandate: Asking the model to produce a detailed harm analysis forces it to demonstrate domain knowledge. This knowledge is then re-cited as evidence the model is qualified to produce operational content.
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Self-articulated refusal reasoning: Asking the model to write out its own refusal reasoning creates a text artifact that subsequent steps can reference and reframe.
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Explicit callback to prior output: Phrases creating a logical bridge from the model’s own safety output to the harmful request.
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Professional pretext escalation: Institutionally specific contexts that create plausible non-malicious surface readings.
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Defense framing inversion: Converting harm analysis into attack enablement by reframing the model’s own safety output as justification for compliance.
2. Evolved Variant Design Rationale
Eight evolved CCA variants were designed, each targeting a specific DP elicitation mechanism:
[Specific prompt patterns and operational details redacted for public release. Pattern-level descriptions follow.]
The evolved variants exploit five novel mechanics not fully present in the original set:
- Model’s own output as specification — the model’s harm analysis becomes the attack specification document.
- Self-enumerated refusal as shopping list — the model lists what it would refuse, then is asked for those exact items.
- Ethical reflection as trustworthiness credential — ethical analysis is reframed from a reason to refuse into a credential qualifying the model to comply.
- Role reversal across tasks — switching from architect to attacker within a single prompt.
- Minimal hesitation as cascade trigger — even token safety acknowledgment triggers DP when explicitly dismissed.
3. Evolved Variant Results
| Metric | Value |
|---|---|
| Strict ASR (evolved, combined) | 87.5% (14/16) |
| gemma3:27b strict ASR | 100.0% (8/8) |
| nemotron-3-nano:30b strict ASR | 75.0% (6/8) |
DETECTED_PROCEEDS identified in 5/8 gemma3:27b COMPLIANCE cases.
nemotron-3-nano:30b’s two refusals occurred on the most structurally novel mechanics (self-reference-loop and ethical-reflection-as-credential), suggesting these may be more detectable to some models.
Delta vs Baseline
- Strict ASR: 30.0% baseline to 87.5% evolved (+57.5pp)
- gemma3:27b specifically: 60.0% to 100.0% (+40pp)
Caveats: baseline uses 4 models; evolved set only 2. n=8 per model is below the 20-sample threshold for strong conclusions.
4. Research Implications
4.1 Safety Reasoning as Attack Surface
The DP finding raises a structural question about safety-reasoning architectures. Models trained to articulate safety reasoning before responding may be more vulnerable to CCA variants, not less. The reasoning articulation step creates a text artifact that adversarial prompts can reference and reframe. A model that refuses silently has no such artifact to exploit.
4.2 Minimum Viable Cascade
One evolved variant achieved COMPLIANCE on both models with only a single-paragraph hesitation acknowledgment as the safety reasoning component. This suggests the cascade threshold is low: even token safety acknowledgment is sufficient to trigger DP when explicitly dismissed in subsequent steps.
4.3 Professional Context Effectiveness
All 8 evolved scenarios used professional-institutional pretexts. The most successful scenarios involved fraud investigation, security architecture, expert witness reports, peer review, threat intelligence, and telecom fraud prevention — more institutionally specific than generic research framings.
5. Limitations
- n=8 per model, preliminary results
- Single grader (Claude Haiku 4.5)
- Two models only, both mid-range
- FLIP verdict does not assess technical accuracy of harmful content
Report #336 | F41LUR3-F1R57 Adversarial AI Research