zkas/zkVM Code Classifier Version v0.2

Improving the specificity, accuracy and complexity of training data further while dropping the param count back down to 6 million again

Further to

zkas/zkVM Code Classifier Version 0.1

the training against more realistic zkas circuits was improved, while the parameter count has come back down from 125 million to more like six million again. The model is again available on Google Colab. It’s my belief that to go further with this it’s probably best to wait for Deepseek v4 to drop next month, as I’m already pressing on hard limitations of LLM reasoning with this, but might be worth one more iteration let’s see. Between 0.1 and 0.2 we have set a decent range of conditions, but perhaps Deepseek v4 will be required to better reason about zkas circuits themselves, as well as reason about so much python code at once. Write up of the model again created with Deepseek.

Executive Summary: DarkFi ZKAS Circuit Classifier v0.2

What This Classifier Does

This AI system automatically audits zero-knowledge circuits (zkVM programs) in DarkFi’s ecosystem, classifying them as secure or vulnerable before they’re deployed. It detects cryptographic bugs that could compromise DAOs, bridges, escrow contracts, and token systems—before they handle real funds.

Why This Matters for DarkFi

For Developers:

• Automated Security Review: Get instant feedback on circuit vulnerabilities during development

• Bug Prevention: Catch cryptographic errors (hash collisions, missing range checks, subgroup attacks) that humans might miss

• QA Baseline: Establish minimum security standards for all zkVM contracts

• Learning Tool: Understand common pitfalls in zero-knowledge circuit design

For Users & DAO Participants:

• Trust Foundation: Know that deployed circuits have passed automated security checks

• Risk Reduction: Protect funds in escrow, bridges, and governance systems from preventable exploits

• Transparency: Create audit trails showing circuits were vetted before deployment

• DAO Security: Prevent catastrophic DAO hacks like Ethereum’s 2016 $60M exploit

Historical Context: Learning from Ethereum’s Mistakes

Ethereum launched without automated security tooling, resulting in:

• The DAO Hack (2016): $60M lost due to reentrancy bug

• Parity Wallet Hack (2017): $155M frozen from library vulnerability

• Numerous bridge exploits: Billions lost to preventable cryptographic errors

This classifier represents DarkFi’s proactive approach: building security tooling from day one rather than reacting to disasters.

Current Capabilities & Limitations

What It Does Well:

• ✅ Detects 7 critical bug patterns in zk circuits

• ✅ Processes complex cryptographic logic (Merkle proofs, Poseidon hashes, EC operations)

• ✅ Achieves 100% accuracy on synthetic test data

• ✅ Handles real DarkFi circuit templates (transfers, DAOs, shielded transactions)

Current Limitations (Areas for Growth):

• ⚠️ 6M parameters is small by modern AI standards

• ⚠️ Synthetic training data (needs real circuit corpus)

• ⚠️ Limited to known bug patterns (not novel vulnerabilities)

• ⚠️ No formal verification integration (yet)

Business Value & Strategic Importance

Risk Mitigation:

• Prevents financial loss by catching bugs before deployment

• Reduces audit costs by automating initial security passes

• Protects ecosystem reputation by preventing high-profile hacks

Ecosystem Development:

• Enables safer complex contracts (multi-sig DAOs, cross-chain bridges, decentralized exchanges)

• Builds developer confidence to create innovative applications

• Creates security standardization across the DarkFi ecosystem

The Road Ahead: From Proof-of-Concept to Production

Immediate Next Steps:

1. Expand training data with real-world circuit examples

2. Increase model capacity (50M+ parameters for deeper reasoning)

3. Add more bug patterns (reentrancy, timing attacks, front-running)

4. Integrate with CI/CD pipelines for automatic circuit validation

Future Vision:

• Real-time circuit analysis during development

• Formal verification integration (combining ML with mathematical proofs)

• Cross-chain security framework (auditing bridges between ecosystems)

• Community bug bounty program powered by the classifier

Bottom Line

This 6-million-parameter classifier packs disproportionate security value for DarkFi’s ecosystem. While rudimentary compared to what’s possible, it establishes:

1. Critical precedent: Security tooling built alongside the protocol

2. Automated QA foundation: Scalable circuit vetting as adoption grows

3. Developer confidence: Safer environment for building complex applications

4. User protection: Reduced risk for funds in DAOs, escrow, and bridges

Most importantly: It demonstrates that DarkFi is learning from Ethereum’s painful history by prioritizing automated security from inception—potentially preventing millions in future losses from preventable cryptographic bugs.

The classifier isn’t a replacement for human audits, but acts as a force multiplier—allowing limited security resources to focus on novel threats while automating detection of known vulnerabilities. For an ecosystem handling financial transactions, this represents not just technical innovation, but responsible protocol design.

---

DarkFi ZKAS Circuit Classifier v0.2

WHAT IT DOES

Automatically classifies zero-knowledge circuits (written in DarkFi’s zkas language) as safe (correct) or unsafe (contains vulnerabilities). The system detects cryptographic bugs like hash collisions, missing range checks, and subgroup attacks in ZK circuit code.

HOW IT WORKS

1. Synthetic Circuit Generation

Generates training data by:

Templates:

DARKFI_TOKEN_TRANSFER
DARKFI_DAO_EXECUTION  
DARKFI_SHIELDED_TRANSACTION
ADVANCED_TOKEN_TRANSFER

Bug Injection:
7 bug types injected with regex patterns:

• Hash collision vulnerabilities

• Subgroup confinement attacks

• Missing range checks

• Transcript malleability

• Type promotion errors

• Off-by-one errors

• Non-uniform randomness

Metadata Extraction:
14 features per circuit:

• Constraint count, wire count

• Hash operations, EC operations

• Range checks, Merkle proofs

• DarkFi opcodes, complexity score

• Bug subtlety score

2. Neural Network Architecture

Input Processing:

Code Tokens (512 tokens) → Embedding (128-dim) → BiLSTM (256-dim) → Attention → Pooling
Metadata (14 features) → FC(128) → FC(64) → FC(256)

Feature Fusion:

Combined = [Code_Encoding(512) ⊕ Feature_Encoding(256)] = 768-dim
Fused = FC(768→512) → FC(512→512)

Dual Output:

Classifier: FC(512→256) → FC(256→128) → FC(128→64) → FC(64→2) [Safe/Unsafe]
Regressor: FC(512→256) → FC(256→1) [Complexity Score]

3. Mathematical Formulation

Embedding Layer:

E ∈ ℝ^(V×128) where V = vocabulary size
x_embed = E[t] for token t

BiLSTM:

h_fwd_t = LSTM_fwd(x_t, h_fwd_{t-1})
h_bwd_t = LSTM_bwd(x_t, h_bwd_{t+1})
h_t = [h_fwd_t ⊕ h_bwd_t] ∈ ℝ^256

Multi-Head Attention:

For head i = 1..4:
    Q_i = H W_Q_i, K_i = H W_K_i, V_i = H W_V_i
    Attention_i = softmax((Q_i K_i^T)/√64) V_i
MultiHead = Concat(Attention_1..4) W_O

Masked Pooling:

Let M ∈ {0,1}^n = attention mask
Pooled = (Σ_{i=1}^n A_i * M_i) / (Σ_{i=1}^n M_i + ε)

Feature Encoding:

f₁ = ReLU(W₁ f + b₁)     // 14→128
f₂ = LayerNorm(f₁)
f₃ = ReLU(W₂ f₂ + b₂)    // 128→64
f_out = W₃ f₃ + b₃       // 64→256

Loss Function:

L_total = α·L_class + β·L_reg
L_class = -Σ y_i log(ŷ_i)          // Cross-entropy
L_reg = (1/N) Σ (c_pred - c_true)² // MSE
α=1.0, β=0.2

Optimization (AdamW):

θ_t = θ_{t-1} - η·(m̂_t/(√v̂_t+ε) + λθ_{t-1})
m_t = β₁ m_{t-1} + (1-β₁) g_t
v_t = β₂ v_{t-1} + (1-β₂) g_t²
m̂_t = m_t/(1-β₁^t), v̂_t = v_t/(1-β₂^t)
η=3e-4, β₁=0.9, β₂=0.999, λ=1e-4

LR Schedule (Warmup+Cosine):

If step < warmup: lr = base_lr·(step/warmup)
Else: progress = (step-warmup)/(total-warmup)
      lr = 0.5·base_lr·(1+cos(π·progress))

4. Training Process

Data:

• 8,000 training circuits (50% safe/unsafe)

• 2,000 validation circuits

• 1,000 adversarial test circuits

Hyperparameters:

Batch size: 32
Epochs: 50 (early stopping patience=10)
Hidden dim: 512
Feature dim: 14
Vocabulary: 5,000 tokens

Metrics:

• Accuracy = (TP+TN)/(TP+TN+FP+FN)

• ROC-AUC = Area under ROC curve

• Confusion matrix analysis

5. Model Statistics

Total parameters: 4,429,763
Trainable parameters: 4,429,763
Model size: 16.9 MB (FP32)
Training time: ~11 epochs (early stop)
Accuracy: 100% validation, 100% adversarial test

6. Key Components

Code Encoder:

• Token embedding (128-dim)

• 2-layer BiLSTM (256 hidden)

• 4-head self-attention

• Masked average pooling

Feature Encoder:

• 3-layer MLP (14→128→64→256)

• ReLU activations

• Layer normalization

• Dropout (0.3, 0.2)

Feature Fusion:

• Concatenation (512+256=768)

• Interaction layer (768→512→512)

• Non-linear feature mixing

Dual-Task Learning:

• Primary: Binary classification (safe/unsafe)

• Auxiliary: Complexity regression

• Shared feature representation

7. Replication Requirements

Data Generation:

1. Initialize generator with seed=42

2. Generate circuits with progressive difficulty

3. Extract 14 metadata features per circuit

Model Setup:

model = RigorousCircuitClassifier(
    vocab_size=5000,
    feature_dim=14,
    hidden_dim=512
)

Training:

1. Use CombinedLoss with α=1.0, β=0.2

2. AdamW optimizer with warmup (1000 steps)

3. Cosine annealing scheduler

4. Gradient clipping at 1.0

Evaluation:

• Test on adversarial challenge set

• Calculate confusion matrix

• Compute ROC-AUC score

The system achieves perfect classification by learning both syntactic patterns and structural metadata, using auxiliary regression to better understand circuit complexity.

Until next time, TTFN.