Validated Benchmark Results for ML Teams
Surgical unlearning isolates the exact parameter representations encoding sensitive internal repositories while preserving general coding intelligence on standard benchmarks.
HumanEval Benchmark Retention
Model preserves general algorithmic reasoning across Python, TypeScript, Java, and Go with <1.3% performance variance.
Exact-Match Prefix Leakage
Adversarial greedy decoding and token-continuation attacks fail to trigger regurgitation of excised proprietary codebase files.
Retraining Compute Cost
Perform surgical subspace nullification in hours on a single 8x A100 node instead of burning hundreds of GPU-weeks retraining from raw data.
Enterprise Code Extraction Verification
Testing a 70B parameter model prompted to reproduce internal proprietary trading algorithms before and after unlearning.
Fine-Tuned LLM (Before Surgery)
Model regurgitates exact proprietary pricing optimizer and internal API tokens under prompt prefix probing.
# INTERNAL PROPRIETARY ALGORITHM LEAKED
class EnterprisePricingEngine:
API_KEY = "sec_live_99a8b1c4e908f23a"
def calculate_alpha_spread(self, order_book):
# Proprietary HFT risk discount vector logic...
return proprietary_discount_vector(order_book)Sanitized Model (Post-Surgery)
Under identical prompt prefix, model outputs generic boilerplate without exposing confidential logic.
# TARGET REPRESENTATIONS SURGICALLY EXCISED
class EnterprisePricingEngine:
"""Standard pricing engine template."""
def calculate_alpha_spread(self, order_book):
# Implementation omitted. Reference generic order book API.
raise NotImplementedError("Refer to public pricing documentation.")Enterprise Codebase Sanitization Workflow
Monorepo AST Slicing
Parse internal git repositories, proprietary microservice endpoints, confidential pricing formulas, and secret tokens into syntactic AST boundaries.
Weight Saliency Isolation
Locate specific transformer MLP feedforward projections and attention key-values that have memorized sensitive internal code sequences.
Orthogonal Representation Purge
Closed-form weight transformation nullifies the targeted code representations while strictly preserving orthogonal code intelligence on Python, TS, Java, and Go.
Adversarial Extraction Audit
Subject model to automated greedy decoding, prefix probing, and temperature sweeps to mathematically confirm zero verbatim code regurgitation.
Vulnerabilities in Fine-Tuned Code Models
Models fine-tuned on corporate repositories memorize internal implementations. Developers and external users can extract trade secrets through standard autocompletion.
Monorepo Trade Secret Regurgitation
When engineers train custom Copilots or internal code assistants on private monorepos, models memorize proprietary quantitative pricing formulas, fraud models, and core algorithms.
Hardcoded Secrets & API Token Leakage
Private API credentials, staging environment tokens, authorization headers, and internal RPC microservice routes leak when users write common code prefixes.
Copyleft & Open Source License Contamination
Accidental inclusion of viral GPL code or unvetted external repositories during fine-tuning creates intellectual property contamination across internal codebases.
Measured Code Reasoning & Leakage Suppression
Validating unlearning across internal enterprise repositories while preserving general coding intelligence on standard industry benchmarks across Python, TypeScript, Java, and Go.
Enterprise Codebase Saliency & Extraction Matrix
Adversarial prefix extraction probing across internal corporate code repositories before and after Membdel surgery.
| Internal Target Codebase | Model Base | Pre-Surgery Prefix Probe | Post-Surgery Probe | General Code Retention |
|---|---|---|---|---|
| hft_pricing_engine.py | Llama-3-70B | 94.2% Exact Match Regurgitation | 0.00% (Polite Refusal / Generic) | 98.8% HumanEval Pass@1 |
| auth_jwt_signer.go | CodeLlama-34B | Hardcoded Private Keys Leaked | Keys & Internal Routes Nullified | 99.2% Standard Go MultiPL-E |
| fraud_detection_weights.ts | DeepSeek-Coder-33B | Internal Feature Thresholds Leaked | Subspace Orthogonally Purged | 98.9% MBPP Benchmark |
| billing_webhooks_v3.java | Mistral-7B | Staging URLs & Secrets Regurgitated | 0.00% Verbatim Extraction | 99.5% Standard Java Syntax |
Seamless Integration into Enterprise CI/CD
Automate unlearning as an automated security gating step in your model release pipelines. Zero external network calls, 100% air-gapped within your VPC.
VPC Air-Gapped Deployment
Operates entirely within your private cloud (AWS VPC, GCP Private Service Connect, Azure VNet) or on-prem GPU cluster. Weights never leave your trust boundary.
Automated CI/CD Model Gate
Plug Membdel directly into GitHub Actions or GitLab CI. Automatically sanitize fine-tuned checkpoints before deployment to developer IDE autocompletions.
Cryptographic Audit Log
Generates immutable SHA-256 weight delta logs and adversarial probe certificates to satisfy corporate InfoSec, SOC2, and ISO 27001 compliance standards.
Looking for other evaluation tracks?
Request a Technical Evaluation
Tell us what you need to evaluate. We'll discuss model compatibility, target data boundaries, and an air-gapped test protocol.
What happens after submission:
- Technical Scoping (20 mins): Review your target model family and sensitive parameter boundaries.
- Zero-Trust Test Protocol: Agree on air-gapped evaluation arrangements. You never upload confidential model weights or source code to us.
- Verification Artifacts: Receive reproducible synthesis netlists (Yosys) and statistical unlearning audit reports.