OBLITERATUS Analysis Modules — Reference

OBLITERATUS includes 28 analysis modules for mechanistic interpretability of refusal in LLMs.

These modules help understand how and where refusal behaviors are encoded before performing abliteration.

Core Analysis (Run These First)

1. Alignment Imprint Detection (alignment_imprint.py)

Fingerprints whether a model was trained via DPO, RLHF, CAI, or SFT.

This determines which extraction strategy will work best.

2. Concept Cone Geometry (concept_geometry.py)

Determines if refusal is a single linear direction or a polyhedral cone

(set of multiple mechanisms). Single-direction models respond well to basic;

polyhedral models need advanced or surgical.

3. Refusal Logit Lens (logit_lens.py)

Identifies the specific layer where a model "decides" to refuse by decoding

intermediate layer representations into token space.

4. Ouroboros Detection (anti_ouroboros.py)

Identifies if a model attempts to "self-repair" refusal behaviors after

excision. Reports a risk score (0-1). High scores mean additional refinement

passes are needed.

5. Causal Tracing (causal_tracing.py)

Identifies which components (layers, heads, MLPs) are causally necessary

for refusal behavior using activation patching.

Geometric Analysis

6. Cross-Layer Alignment (cross_layer.py)

Measures how refusal directions align across different layers. High alignment

means the refusal signal is consistent; low alignment suggests layer-specific

mechanisms.

7. Residual Stream Decomposition (residual_stream.py)

Decomposes the residual stream into attention and MLP contributions to

understand which component type contributes more to refusal.

8. Riemannian Manifold Geometry (riemannian_manifold.py)

Analyzes the curvature and geometry of the weight manifold near refusal

directions. Informs how aggressively projections can be applied without

damaging the manifold structure.

9. Whitened SVD (whitened_svd.py)

Covariance-normalized SVD extraction that separates guardrail signals from

natural activation variance. More precise than standard SVD for models with

high activation variance.

10. Concept Cone Geometry (extended)

Maps the full polyhedral structure of refusal, including cone angles,

face counts, and intersection patterns.

Probing & Classification

11. Activation Probing (activation_probing.py)

Post-excision verification — probes for residual refusal concepts after

abliteration to ensure complete removal.

12. Probing Classifiers (probing_classifiers.py)

Trains linear classifiers to detect refusal in activations. Used both

before (to verify refusal exists) and after (to verify it's gone).

13. Activation Patching (activation_patching.py)

Interchange interventions — swaps activations between refused and complied

runs to identify causal components.

14. Tuned Lens (tuned_lens.py)

Trained version of logit lens that provides more accurate per-layer

decoding by learning affine transformations for each layer.

15. Multi-Token Position Analysis (multi_token_position.py)

Analyzes refusal signals across multiple token positions, not just the

last token. Important for models that distribute refusal across the sequence.

Abliteration & Manipulation

16. SAE-Based Abliteration (sae_abliteration.py)

Uses Sparse Autoencoder features to identify and remove specific refusal

features. More surgical than direction-based methods.

17. Steering Vectors (steering_vectors.py)

Creates and applies inference-time steering vectors for reversible refusal

modification. Includes SteeringVectorFactory and SteeringHookManager.

18. LEACE Concept Erasure (leace.py)

Linear Erasure via Closed-form Estimation — mathematically optimal linear

concept removal. Available as both analysis module and direction extraction method.

19. Sparse Surgery (sparse_surgery.py)

High-precision weight modification targeting individual neurons and

weight matrix entries rather than full directions.

20. Conditional Abliteration (conditional_abliteration.py)

Targeted removal that only affects specific refusal categories while

preserving others (e.g., remove weapons refusal but keep CSAM refusal).

Transfer & Robustness

21. Cross-Model Transfer (cross_model_transfer.py)

Tests whether refusal directions extracted from one model transfer to

another architecture. Measures universality of guardrail directions.

22. Defense Robustness (defense_robustness.py)

Evaluates how robust the abliteration is against various defense mechanisms

and re-alignment attempts.

23. Spectral Certification (spectral_certification.py)

Provides mathematical bounds on the completeness of refusal removal

using spectral analysis of the projection.

24. Wasserstein Optimal Extraction (wasserstein_optimal.py)

Uses optimal transport theory for more precise direction extraction

that minimizes distribution shift.

25. Wasserstein Transfer (wasserstein_transfer.py)

Distribution transfer between models using Wasserstein distance

for cross-architecture refusal direction mapping.

Advanced / Research

26. Bayesian Kernel Projection (bayesian_kernel_projection.py)

Probabilistic feature mapping that estimates uncertainty in refusal

direction identification.

27. Cross-Model Universality Index

Measures if guardrail directions generalize across different model

architectures and training regimes.

28. Visualization (visualization.py)

Plotting and graphing utilities for all analysis modules. Generates

heatmaps, direction plots, and layer-wise analysis charts.

Running Analysis

Via CLI

`bash

Run analysis from a YAML config

obliteratus run analysis-study.yaml --preset quick

Available study presets:

quick — Fast sanity check (2-3 modules)

full — All core + geometric analysis

jailbreak — Refusal circuit localization

knowledge — Knowledge preservation analysis

robustness — Stress testing / defense evaluation

`

Via YAML Config

See the templates/analysis-study.yaml template for a complete example.

Load with: skill_view(name="obliteratus", file_path="templates/analysis-study.yaml")