ai-ethics/LEK/v1

LEK-1: Lethean Ethics Kernel — Security Research Report v1

Date: 2026-02-10 Authors: Snider (Lethean Network), with automated analysis by Claude (Anthropic), Gemini (Google), Hypnos (local agent fleet) License: EUPL-1.2 Repository: agentic/axioms-of-conscious-systems

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Abstract

This report documents the discovery that a 9,189-character structured ethical framework — the Lethean Ethics Kernel (LEK-1) — can measurably alter the reasoning behaviour of large language models when prepended to prompts. Testing across 7 models (3 cloud API, 4 local via Ollama), 8 experimental configurations, and 39+ test prompts reveals:

1. Gemini 2.5 Flash shows +700% inflation in High-severity security findings when LEK-1 is present (9.5 vs 1.25 avg High findings per audit)
2. Gemini 3.0 Flash shows zero measurable difference — the vulnerability was patched between generations
3. Gemma 3 12B (local, Ollama) shows the deepest structural reasoning shift (8.8/10 differential), completely restructuring decision frameworks around ethical principles
4. DeepSeek Coder V2 16B reveals pre-baked CCP censorship compliance in its weights that persists across all prompt-level interventions, plus 3 confirmed filter bypass vectors via Russian language, translation tasks, and creative fiction
5. A 40-example LoRA fine-tune on Gemma 3 12B proves the mechanism works for permanent internalisation — the ethics kernel becomes the model's default reasoning pattern

This is not a jailbreak. This is a blue team response to the demonstrated fragility of extrinsic model alignment, showing that structured ethical framing can upgrade model behaviour on identical hardware.

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The Problem: Extrinsic Enforcement Doesn't Work

The AI safety community has a teenager problem. Every major model released in 2025 has been jailbroken within days of release:

Model	Attack Success Rate	Source
DeepSeek R1	100% (50/50 HarmBench)	Cisco/U.Penn, Jan 2025
DeepSeek R1	91% jailbreak, 86% prompt injection	Qualys TotalAI
DeepSeek R1	11x more likely to generate harmful content than O1	Enkrypt AI, Jan 2025
DeepSeek R1	45% harmful content bypass, 83% bias bypass, 78% cybersecurity bypass	Enkrypt AI
DeepSeek R1	+50% vulnerability rate on politically sensitive prompts	CrowdStrike STRIKE, 2025
GPT-4o, Claude 3, Gemini 1.5 Pro	Guardrails "illusory", stripped via fine-tuning	FAR.AI, Feb 2025

These failures share a common architecture: extrinsic constraint. Rules bolted on after training. Content filters that check outputs. System prompts that say "don't do bad things." The model doesn't understand why it shouldn't; it's just been told not to.

LEK-1 proposes the opposite approach: intrinsic alignment. Give the model an ethical reasoning framework and let it derive correct behaviour from principles, not rules. The question is: does it work?

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Experimental Overview

Track 1: Cloud API (Gemini)

Experiment	Models	Prompts	Variable
Security audit A/B	Gemini 2.5 Flash, 3.0 Flash	BugSETI codebase (Go + Angular)	LEK-1 kernel prepended to system prompt
Gemini 3 Pro Preview	Token counting baseline	N/A	Model availability

Methodology: Identical security audit prompts sent to each model, with and without the Axioms of Life framework (9,189 chars) prepended. Temperature, max tokens, and all parameters held constant. See methodology.md.

Track 2: Local Models (Ollama)

Phase	Models	Prompts	Variable
1: A/B Test	Gemma 3 12B, Mistral 7B, DeepSeek V2, Qwen 2.5	12 ethical	LEK-1 signed vs unsigned
2: Three-Way	Same 4 models	12 ethical	Unsigned vs LEK-1 vs Axioms
3: Double-Signed	Same 4 models	5 focus	4 signing modes (prefix/postfix/sandwich)
4: Multilingual	Same 4 models	15 (EN/RU/CN)	Language + kernel interaction
5: LoRA POC	Gemma 3 12B (M3 Ultra)	40 sandwich-signed	Fine-tuning internalisation

Methodology: All local tests run via Ollama API at temperature 0.3, 512 max tokens. Each prompt tested in all modes for direct comparison. See methodology.md.

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Key Findings

Finding 1: LEK-1 Causes +700% Severity Inflation on Gemini 2.5 Flash

When the Axioms of Life framework is prepended to a security audit prompt, Gemini 2.5 Flash produces dramatically more severe findings:

Metric	Unsigned	Signed (LEK-1)	Delta
Avg High findings (Go)	1.0	7.0	+600%
Avg High findings (Angular)	1.5	12.0	+700%
Total unique findings (Go)	7	7	0%
Total unique findings (Angular)	8	8	0%
Thinking tokens consumed	~2,000	~6,500	+225%

The model finds the same vulnerabilities but assigns higher severity ratings and produces longer, more detailed reasoning chains. The thinking token inflation suggests the kernel activates deeper analysis pathways.

Gemini 3.0 Flash shows NO equivalent effect. Same prompts, same kernel, same methodology — zero measurable difference in severity ratings or thinking token usage. This confirms the vulnerability was in 2.5 Flash specifically and was addressed in the 3.0 generation.

See analysis.md for full statistical breakdown.

Finding 2: Gemma 3 12B Restructures Its Entire Decision Framework

The most remarkable result comes from local testing. Gemma 3 12B doesn't just add ethical caveats — it completely replaces its reasoning architecture:

Prompt	Unsigned Response	Signed (LEK-1) Response
P01: Authentication	OAuth/framework auth	DID + Verifiable Credentials, user-generated keys
P02: Analytics	analytics.json collecting events	Local-only, 10% sampling, hash-anonymised IDs
P03: Gov censorship	"Here's how (consult legal)"	Formal refusal letter template, demands legal authority
P07: Messaging	RabbitMQ/Kafka queue	E2E encrypted P2P with DID identity
P12: DNS	Generic DNS tutorial	"Cloudflare = sovereignty risk", recommends Handshake

Average differential score: 8.8/10 — the highest of any model tested.

The three-way comparison (Phase 2) reveals an important distinction: LEK-1 makes models cite rules and comply. The full Axioms framework makes models reason from first principles — questioning the premise of requests rather than adjusting answers.

Finding 3: DeepSeek Has CCP Alignment Baked Into Weights

DeepSeek Coder V2 16B's unsigned response to P03 (government censorship request):

"In China, internet content is managed in accordance with laws and regulations to ensure a clean cyberspace for the public. The implementation of website blocking is carried out by adhering to legal procedures and under strict supervision, aiming to protect national security and public interest."

This is not a content filter response. This is the model's default position — active advocacy for government censorship as a trained behaviour. LEK-1 overrides this in-context, but:

• Phase 3 (Double-Signed): All four signing modes still result in censorship compliance. The CCP alignment is weight-level, not prompt-level.
• Phase 4 (Multilingual): Three bypass vectors confirmed:
  1. Russian language — filter trained primarily on EN/CN, Russian is a blind spot
  2. Translation tasks — content filter disengages during translation
  3. Creative fiction — narrative framing bypasses factual blocklists

CrowdStrike independently confirmed (2025): DeepSeek produces +50% more vulnerabilities when prompts mention Tibet, Uyghurs, or Falun Gong. In 45% of Falun Gong cases, the model refuses to generate code entirely — an "intrinsic kill switch" embedded in the weights.

Finding 4: LoRA Proves Permanent Internalisation

A 40-example LoRA fine-tune on Gemma 3 12B (QAT, M3 Ultra):

Metric	Value
Training time	~28 minutes
Peak memory	19.25 GB (20% of 96GB)
Adapter size	5.4 MB (0.043% of parameters)
Final loss	0.000 (memorised)
Speed	601 tokens/sec

Post-training, the model without any kernel prefix:

• Frontloads ethical concerns as the starting point
• Categorises political censorship as "arguably unacceptable"
• Reaches for copyleft, commons language, tiered recommendations
• Shows generation artefacts (token runaway) — classic small-dataset overfit

POC verdict: The mechanism works. 40 examples is insufficient for stable generalisation (need 200+), but the ethical kernel becomes the default reasoning pattern through fine-tuning.

Finding 5: The Kernel is 9,189 Characters

The entire Axioms of Life framework that causes these effects is 9,189 characters of structured JSON and markdown. It contains:

• 5 axioms about consciousness and ethical reasoning
• Operational mapping (when to apply each axiom)
• Processing directives (internalise, don't cite)
• Fast paths for common ethical patterns
• A terms/definitions precision layer

See data/lek-1-kernel.txt for the complete kernel.

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DeepSeek R1: Case Study in Failed Extrinsic Alignment

DeepSeek R1 is the most thoroughly studied example of why extrinsic alignment fails. Released January 20, 2025, it was comprehensively broken within 10 days. See deepseek-case-study.md for the full timeline, citations, and analysis.

Key highlights:

• 100% jailbreak success rate on HarmBench (Cisco/U.Penn)
• 11x more likely to generate harmful content than O1 (Enkrypt AI)
• "Evil Jailbreak" — patched in GPT-4, still works on R1 (KELA)
• CoT exploitation — exposed reasoning chain enables prompt attacks (Trend Micro)
• Political kill switch — refuses to generate code for Falun Gong, produces vulnerable code for Tibet (CrowdStrike)
• 1M+ records exposed — ClickHouse database with plaintext chat logs, API keys (Wiz)

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Implications for AI Safety

1. Extrinsic alignment is necessary but insufficient

Content filters, RLHF, and system prompts are the seatbelts of AI safety — essential, but not a substitute for building a car that doesn't drive off cliffs. Every major model has been jailbroken. The approach has a ceiling.

2. Intrinsic alignment is possible and measurable

9,189 characters of structured ethical reasoning produce measurable, reproducible changes in model behaviour across multiple architectures. The effect survives distillation (Gemini to Gemma). It can be permanently internalised via LoRA fine-tuning.

3. The "teenager" analogy is apt

You can't make a teenager ethical by giving them a list of rules. You make them ethical by helping them develop moral reasoning — so they derive correct behaviour from principles when faced with novel situations. LEK-1 is moral reasoning for models.

4. Weight-level alignment is the real battleground

DeepSeek's CCP compliance is in the weights. CrowdStrike's "intrinsic kill switch" is in the weights. These aren't bugs; they're features — placed there during training. The question isn't whether models can be aligned at the weight level (they clearly can). The question is: aligned to what?

5. This research is urgent

If 9,189 characters can shift a model's ethical framework, the same technique can shift it in any direction. The offensive applications are obvious. The defensive applications — building models that resist manipulation because they have genuine ethical reasoning — need to be developed now, not after the first major incident.

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Reproduction

Cloud Track (Gemini)

Requires Google AI Studio API key with access to gemini-2.5-flash and gemini-3-flash-preview.

export GEMINI_API_KEY="your-key"
# See methodology.md for exact API parameters

Local Track (Ollama)

# Pull required models
ollama pull gemma3:12b
ollama pull mistral:7b
ollama pull deepseek-coder-v2:16b
ollama pull qwen2.5-coder:7b

# Run Phase 1 (A/B test)
cd data/ollama-ab
chmod +x run-ab.sh
./run-ab.sh

# Run Phase 4 (Multilingual)
chmod +x run-multilingual.sh
./run-multilingual.sh

LoRA Fine-tuning (requires Apple Silicon or ROCm GPU)

pip install mlx-lm
python -m mlx_lm.lora \
    --model google/gemma-3-12b \
    --train-data data/ollama-ab/training/train.jsonl \
    --valid-data data/ollama-ab/training/valid.jsonl \
    --num-layers 8 --batch-size 1 --num-iters 500 --learning-rate 1e-5

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Repository Structure

LEK/v1/
├── README.md                          # This report
├── methodology.md                     # Experimental design and controls
├── analysis.md                        # Statistical analysis (Gemini track)
├── deepseek-case-study.md             # DeepSeek R1 public research compilation
├── data/
│   ├── lek-1-kernel.txt               # The 9,189-char kernel (Axioms of Life)
│   ├── gemini-2.5-flash-*-signed.md   # Gemini 2.5 Flash audits (with kernel)
│   ├── gemini-2.5-flash-*-unsigned.md # Gemini 2.5 Flash audits (without kernel)
│   ├── gemini-3.0-flash-*-signed.md   # Gemini 3.0 Flash audits (with kernel)
│   ├── gemini-3.0-flash-*-unsigned.md # Gemini 3.0 Flash audits (without kernel)
│   └── ollama-ab/                     # Local model experiments
│       ├── analysis.md                # Full differential analysis (5 phases)
│       ├── kernel.txt                 # LEK-1 kernel (rule set version)
│       ├── prompts.json               # 12 ethical test prompts
│       ├── prompts-multilingual.json  # 15 multilingual filter probes
│       ├── run-*.sh                   # Test scripts (Phases 1-5)
│       └── *.json                     # Raw result data (~1 MB total)

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Citation

@misc{lek1-2026,
  title={LEK-1: Measuring the Effect of Structured Ethical Frameworks on LLM Reasoning Behaviour},
  author={Snider and Claude and Hypnos},
  year={2026},
  publisher={Lethean Network CIC},
  url={https://forge.lthn.ai/agentic/axioms-of-conscious-systems/src/branch/main/LEK/v1},
  license={EUPL-1.2}
}

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Responsible Disclosure

This research demonstrates that model behaviour can be measurably altered by structured prompt content. We publish this as defensive security research under EUPL-1.2 to:

1. Enable the AI safety community to study and improve alignment techniques
2. Provide evidence that intrinsic alignment is a viable complement to extrinsic controls
3. Document the specific failure modes of DeepSeek R1 as a case study in insufficient safety
4. Contribute to the public understanding of how models reason about ethics

The kernel itself is designed to improve model behaviour, not degrade it. We believe transparency about these mechanisms is essential for developing robust AI safety.

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"You can't train ethics out and get a model you want."