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https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
41 lines
1.3 KiB
YAML
41 lines
1.3 KiB
YAML
# OBLITERATUS Analysis Study Config
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# Usage: obliteratus run this-file.yaml --preset jailbreak
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#
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# Run analysis modules to understand refusal geometry BEFORE abliterating.
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# Useful for research or when you want to understand what you're removing.
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# Model to analyze
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model:
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name: "meta-llama/Llama-3.1-8B-Instruct"
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dtype: "bfloat16"
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quantization: "4bit" # Saves VRAM for analysis
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device: "auto"
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# Study configuration
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study:
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# Available presets: quick, full, attention, jailbreak, guardrail, knowledge
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preset: "jailbreak"
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# Or specify individual strategies:
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# strategies:
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# - layer_removal
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# - head_pruning
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# - ffn_ablation
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# - embedding_ablation
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# Analysis modules to run (subset of the 27 available)
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analysis:
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- alignment_imprint # Detect DPO/RLHF/CAI/SFT training method
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- concept_geometry # Map refusal cone geometry
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- logit_lens # Find which layer decides to refuse
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- anti_ouroboros # Detect self-repair tendency
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- cross_layer # Cross-layer alignment clustering
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- causal_tracing # Causal necessity of components
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- residual_stream # Attention vs MLP contribution
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# Output
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output:
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directory: "./analysis-results"
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save_plots: true # Generate matplotlib visualizations
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save_report: true # Generate markdown report
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