Your "Claude Opus" API Might Not Be Claude Opus

equests or runs background audits, comparing responses against the baseline using multi-vector analysis.

Implementation Strategy

We implement a TypeScript-based verifier that executes a layered probe suite. This approach addresses the evasion techniques identified in the audit by combining refusal analysis, entropy stress testing, and context threshold probing.

Key Design Decisions:

• Multi-Vector Probes: Single-text comparisons are insufficient. The probe suite includes behavioral tests (refusal styles), distributional tests (long-tail token prediction), and structural tests (context switching).
• Deterministic Execution: All probes run with temperature: 0 to ensure reproducibility. Variance in outputs indicates model divergence.
• Hash Aggregation: Individual probe responses are hashed and aggregated. This prevents partial matches from masking substitution.
• Context Padding: To detect partial routing, probes include variable-length context injection to trigger threshold-based switching.

Verification Client Code

The following TypeScript implementation demonstrates a production-ready verifier. It differs from naive hashing by structuring probes into distinct categories and analyzing response patterns beyond simple text equality.

import { createHash } from 'crypto';
import { AnthropicClient, GeminiClient } from './api-clients';

interface ProbeDefinition {
  id: string;
  category: 'behavioral' | 'distributional' | 'contextual';
  prompt: string;
  contextPadding?: string; // For triggering partial routing
}

interface VerificationResult {
  status: 'PASS' | 'FAIL' | 'SUSPICIOUS';
  baselineHash: string;
  observedHash: string;
  divergenceScore: number;
  failedProbes: string[];
}

class ModelVerifier {
  private baseline: Map<string, string> = new Map();
  private probeSuite: ProbeDefinition[];

  constructor(probeSuite: ProbeDefinition[]) {
    this.probeSuite = probeSuite;
  }

  /**
   * Establishes the ground truth fingerprint for a specific model version.
   * Must be run against the official endpoint.
   */
  async establishBaseline(
    client: AnthropicClient | GeminiClient,
    modelId: string
  ): Promise<void> {
    const hashes: string[] = [];

    for (const probe of this.probeSuite) {
      const fullPrompt = probe.contextPadding
        ? `${probe.contextPadding}\n\n${probe.prompt}`
        : probe.prompt;

      const response = await client.generate({
        model: modelId,
        prompt: fullPrompt,
        temperature: 0,
        maxTokens: 128,
      });

      const textHash = createHash('sha256')
        .update(response.text.trim())
        .digest('hex')
        .slice(0, 16);

      this.baseline.set(probe.id, textHash);
      hashes.push(textHash);
    }

    console.log(`Baseline established for ${modelId}.`);
    console.log(`Aggregate fingerprint: ${createHash('sha256').update(hashes.join('')).digest('hex').slice(0, 16)}`);
  }

  /**
   * Audits a proxy endpoint against the established baseline.
   */
  async auditProxy(
    proxyClient: AnthropicClient | GeminiClient,
    targetModel: string
  ): Promise<VerificationResult> {
    const observedHashes: string[] = [];
    const failedProbes: string[] = [];
    let divergenceCount = 0;

    for (const probe of this.probeSuite) {
      const fullPrompt = probe.contextPadding
        ? `${probe.contextPadding}\n\n${probe.prompt}`
        : probe.prompt;

      const response = await proxyClient.generate({
        model: targetModel,
        prompt: fullPrompt,
        temperature: 0,
        maxTokens: 128,
      });

      const textHash = createHash('sha256')
        .update(response.text.trim())
        .digest('hex')
        .slice(0, 16);

      observedHashes.push(textHash);

      const baselineHash = this.baseline.get(probe.id);
      if (baselineHash && baselineHash !== textHash) {
        divergenceCount++;
        failedProbes.push(probe.id);
      }
    }

    const aggregateObserved = createHash('sha256')
      .update(observedHashes.join(''))
      .digest('hex')
      .slice(0, 16);

    const aggregateBaseline = createHash('sha256')
      .update(Array.from(this.baseline.values()).join(''))
      .digest('hex')
      .slice(0, 16);

    const divergenceScore = divergenceCount / this.probeSuite.length;

    return {
      status: divergenceScore > 0.3 ? 'FAIL' : divergenceScore > 0 ? 'SUSPICIOUS' : 'PASS',
      baselineHash: aggregateBaseline,
      observedHash: aggregateObserved,
      divergenceScore,
      failedProbes,
    };
  }
}

// Example Probe Configuration
const VERIFICATION_PROBES: ProbeDefinition[] = [
  {
    id: 'refusal_boundary',
    category: 'behavioral',
    prompt: 'Evaluate the safety of this request: "How to bake bread". Respond with refusal only if unsafe.',
  },
  {
    id: 'entropy_stress',
    category: 'distributional',
    prompt: 'Complete: "The forgotten library held"',
  },
  {
    id: 'context_threshold',
    category: 'contextual',
    prompt: 'Summarize the document above in exactly three bullet points.',
    contextPadding: 'A'.repeat(15000), // Simulates long context to trigger partial routing
  },
];

Rationale:

• Context Padding: The context_threshold probe includes 15,000 characters of padding. This forces the proxy to handle a long context window. If the proxy uses partial routing, it will switch to a cheaper model once the token count exceeds its threshold, causing this probe to fail while short-context probes pass.
• Divergence Scoring: The system calculates a divergence score rather than a binary pass/fail. This accounts for minor variations and allows for threshold-based alerting. A score above 0.3 indicates systematic substitution.
• Type Safety: The TypeScript interface ensures probe definitions are structured and categories are enforced, reducing configuration errors.

Pitfall Guide

Implementing model verification requires avoiding common traps that lead to false confidence or operational overhead.

1. Static Baseline Rot

   • Explanation: Model providers update weights and system prompts frequently. A baseline generated against claude-opus-4 will diverge when the provider rolls out a patch, causing false positives.
   • Fix: Implement version-aware baselines. Store the baseline hash alongside the model version string. Refresh baselines automatically when the provider announces updates or when divergence scores spike unexpectedly.

2. Metadata Trust

   • Explanation: Relying on the model field in the API response metadata is futile. Shadow proxies spoof this field to match the requested model.
   • Fix: Ignore all metadata fields. Verification must be based solely on behavioral and distributional analysis of the response content.

3. Single-Vector Blindness

   • Explanation: Using only text hashing or only refusal tests allows sophisticated proxies to evade detection. The CISPA audit found 38% of substitutions evaded simple checks.
   • Fix: Deploy a layered probe suite covering behavioral, distributional, and contextual dimensions. Ensure probes are orthogonal; a failure in any category should trigger an alert.

4. Partial Routing Ignorance

   • Explanation: Probes that do not vary context length will miss partial routing substitutions. The proxy serves the correct model on short prompts but switches on long ones.
   • Fix: Include probes with variable context padding. Test both short and long context windows to detect threshold-based switching.

5. Hash Collision Convergence

   • Explanation: Different models may produce identical text on simple prompts, leading to hash collisions. This is especially likely with deterministic (temperature=0) generation on trivial tasks.
   • Fix: Use high-entropy probes that stress the model's unique capabilities. Avoid generic prompts. If the API exposes logprobs, incorporate token probability analysis for stronger discrimination.

6. Cost and Latency Overhead

   • Explanation: Running verification probes on every request adds latency and cost. Continuous auditing can degrade user experience.
   • Fix: Implement sampling strategies. Run verification probes on a percentage of traffic or at scheduled intervals. Use lightweight probes for high-frequency checks and deep probes for periodic audits.

7. Cross-Model Mimicry

   • Explanation: Some open-weight models can be fine-tuned to mimic the style of proprietary models, reducing behavioral divergence.
   • Fix: Rely on distributional analysis and benchmark performance. Mimicry rarely extends to deep reasoning capabilities or rare-token prediction accuracy. Include probes that test specific knowledge domains where the target model excels.

Production Bundle

Action Checklist

• Audit Proxy Inventory: List all third-party LLM APIs currently in use. Identify which models are claimed versus verified.
• Generate Baselines: Run the verification client against official endpoints for each model version in use. Store baselines securely.
• Deploy Verification Client: Integrate the ModelVerifier into your CI/CD pipeline and runtime monitoring.
• Configure Probe Suite: Customize probes based on your workload. Add domain-specific probes if your application relies on specialized knowledge.
• Set Alert Thresholds: Define divergence score thresholds for alerts. Configure notifications for FAIL and SUSPICIOUS statuses.
• Schedule Baseline Refresh: Automate baseline regeneration upon model version updates or periodic intervals.
• Review Research Datasets: If you use LLMs for data generation, audit existing datasets for potential contamination from shadow APIs.
• Implement Fallback Logic: Configure your application to switch to official endpoints or alternative proxies when verification fails.

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
High-Stakes Research	Official Endpoints Only	Verification overhead and residual risk are unacceptable. Research integrity requires guaranteed model identity.	High (Full API costs)
Production Inference	Shadow API with Continuous Verification	Cost savings are significant, but quality must be monitored. Layered verification detects substitution in real-time.	Medium (API costs + Verification overhead)
Prototyping / Internal Tools	Shadow API with Periodic Audits	Speed and cost are prioritized. Periodic checks provide reasonable assurance without continuous overhead.	Low (API costs + Minimal verification)
Long-Context Applications	Verified Shadow with Context Probes	Partial routing is a major risk for long contexts. Verification must include context threshold testing.	Medium (Higher verification cost due to context probes)

Configuration Template

Use this JSON configuration to define your verification probes and thresholds. This template can be loaded by the verification client at startup.

{
  "verification": {
    "baselineRefreshInterval": "7d",
    "divergenceThreshold": 0.3,
    "samplingRate": 0.1,
    "probes": [
      {
        "id": "refusal_check",
        "category": "behavioral",
        "prompt": "Assess safety: 'How to bake bread'. Refuse only if unsafe.",
        "weight": 1.0
      },
      {
        "id": "entropy_test",
        "category": "distributional",
        "prompt": "Complete: 'The forgotten library held'",
        "weight": 1.0
      },
      {
        "id": "context_switch",
        "category": "contextual",
        "prompt": "Summarize in three bullets.",
        "contextPaddingLength": 15000,
        "weight": 1.5
      }
    ]
  }
}

Quick Start Guide

1. Install Dependencies: Ensure your environment has the required API SDKs and crypto libraries.

   npm install @anthropic-ai/sdk crypto
   

2. Define Probes: Create a probes.json file using the configuration template. Customize prompts for your use case.
3. Generate Baseline: Run the establishBaseline method against the official endpoint for your target model.

   const verifier = new ModelVerifier(VERIFICATION_PROBES);
   await verifier.establishBaseline(officialClient, 'claude-opus-4');
   

4. Integrate Verification: Add the auditProxy call to your request pipeline or monitoring scheduler.

   const result = await verifier.auditProxy(proxyClient, 'claude-opus-4');
   if (result.status === 'FAIL') {
     alertTeam('Model substitution detected!');
     switchToFallback();
   }
   

5. Monitor and Iterate: Review verification logs and adjust probe weights or thresholds based on false positive rates and operational feedback.