During our recent penetration test of a major Indian fintech aggregator, we observed that traditional fuzzing and Burp Suite Intruder payloads were consistently failing against their GraphQL and nested JSON APIs. The issue wasn't the lack of coverage, but the lack of semantic awareness. Standard wordlists don't understand the relationship between a transaction_id and a user_context_hash. To bridge this gap, we implemented a "Shadow Repeater" architecture—a methodology that mirrors live production traffic to an isolated environment where an LLM (Large Language Model) mutates the requests in real-time for manual verification.
What is Shadow Repeater?
Shadow Repeater is not a single tool but a design pattern for modern API security testing. It involves mirroring production or staging traffic to a "shadow" proxy. This proxy uses an AI engine to perform semantic mutation of the request parameters. Unlike traditional repeaters that replay the exact same request or simple regex replacements, Shadow Repeater understands the intent of the API call. This methodology is similar to techniques used for detecting crypto-stealing C2 traffic in enterprise networks.
I found that by using local instances of Llama-3-70B, we could generate payloads that bypassed input validation filters by maintaining the expected data structure while injecting subtle logical flaws. This is particularly effective for discovering Broken Object Level Authorization (BOLA), a critical risk in the OWASP Top 10, and complex business logic vulnerabilities that automated scanners miss.
Why Use Shadow Repeater in Your Workflow?
Manual testing is slow, and automated testing is often "dumb." Shadow Repeater provides a middle ground. We use it to:
- Automate Contextual Fuzzing: The AI understands that a field named
emailneeds a different mutation strategy than a field namedamount_inr. - Bypass WAF Fingerprinting: By mutating headers and payload structures realistically, we avoid triggering basic rate limits or signature-based blocks.
- Ensure DPDP Compliance: In the Indian context, the Digital Personal Data Protection (DPDP) Act 2023 mandates strict handling of PII. Shadow Repeater allows us to mask sensitive data locally before it ever touches a testing log or an external AI API.
System Requirements
To run a Shadow Repeater setup effectively, especially when hosting local LLMs for data residency compliance and managing secure SSH access for teams across distributed environments, I recommend the following hardware:
- CPU: 16-core modern processor (AMD EPYC or Intel Xeon for server-side).
- GPU: Minimum 2x NVIDIA A100 or 4090 (24GB VRAM) to handle concurrent inference and mutation.
- Memory: 128GB RAM to manage large traffic buffers.
- OS: Ubuntu 22.04 LTS or any Debian-based distribution.
How to Install Shadow Repeater Components
We start by installing GoReplay (gor) to handle the traffic mirroring. This is the backbone of the "Shadow" aspect.
Download and install GoReplay
wget https://github.com/buger/goreplay/releases/download/v1.3.3/gor_1.3.3_x64.tar.gz tar -xvf gor_1.3.3_x64.tar.gz sudo mv gor /usr/local/bin/
Verify installation
gor --version
Next, we set up the AI-enhanced proxy. I prefer using a custom Python middleware that interfaces with a local Ollama instance. This ensures that no data leaves the internal network, keeping us compliant with Indian data localization laws.
Install Ollama for local LLM inference
curl -fsSL https://ollama.com/install.sh | sh
Pull the Llama-3 model
ollama run llama3:70b
Initial Configuration and Interface Overview
The architecture requires an Nginx entry point to mirror the traffic. We modify the Nginx configuration of the staging environment to send a copy of every request to our shadow analyzer.
Example Nginx Mirror Configuration
location /api/v1 { mirror /mirror; proxy_pass http://backend_production; }
location = /mirror { internal; proxy_pass http://ai_shadow_repeater_engine:5000; proxy_set_header X-Shadow-Mirror "true"; proxy_set_header X-Original-IP $remote_addr; }
Preparing Your Source Assets
Before starting the Shadow Repeater tutorial, we must identify the high-value targets. In Indian e-commerce ecosystems, these are typically the /checkout, /payment/initiate, and /user/profile endpoints. We use nmap to map the API surface and identify any exposed Swagger/OpenAPI documentation.
$ nmap -sV --script http-api-dot-directory --script-args 'http-api-dot-directory.basepath=/v2/' api.target.in
If a Swagger UI is exposed, be wary of CVE-2023-45133, which can be researched via the NIST NVD. We observed that many internal shadow testing environments accidentally expose these interfaces, leading to full API documentation leaks.
Applying the Shadow Repeater Effect
Once the traffic is mirrored, we use GoReplay to pipe the raw HTTP requests into our AI mutation engine. The following command captures traffic on port 80 and sends it to our local proxy, specifically filtering for POST requests that contain an Authorization header.
gor --input-raw :80 --output-http "http://ai-analyzer-proxy.local:5000" --http-allow-method POST --http-allow-header "Authorization: Bearer.*"
Configuring Basic Parameters: Count, Offset, and Scale
In the context of Shadow Repeater, these terms refer to the mutation intensity:
- Count: The number of mutated "shadow" requests generated for every single real request. I typically set this to 5 for manual review.
- Offset: The delay between the original request and the shadow requests. This prevents race conditions in the database.
- Scale: The "distance" of the mutation. A low scale changes a few characters; a high scale completely restructures the JSON body.
Adjusting Opacity and Blending Modes
In security testing, "Opacity" refers to how visible our shadow traffic is to the target's SOC (Security Operations Center). For organizations using a SIEM for threat detection, "Blending" refers to how well the mutated traffic mimics legitimate user behavior. We use the following Python snippet in our proxy to adjust the blending of the User-Agent and other fingerprintable headers.
def blend_headers(original_headers, mutation_profile): # Mimic common Indian mobile ISP headers (JIO/Airtel) if mutation_profile == "mobile_in": original_headers['User-Agent'] = "Mozilla/5.0 (Linux; Android 13) AppleWebKit/537.36..." original_headers['X-Network-Type'] = "4G" return original_headers
Creating Realistic Depth with Gradient Shadows
"Gradient Shadows" in API testing refers to the incremental mutation of nested objects. Instead of changing the entire payload at once, we mutate one level of the JSON hierarchy at a time. This helps pinpoint exactly which object level lacks authorization checks.
import json
def generate_gradient_shadows(payload): data = json.loads(payload) mutations = [] # Level 1 mutation m1 = data.copy() m1['user_id'] = "1002" mutations.append(m1) # Level 2 mutation (nested) m2 = data.copy() if 'order_details' in m2: m2['order_details']['merchant_id'] = "attacker_id" mutations.append(m2) return mutations
Using Keyframes for Dynamic Shadow Animation
For stateful APIs, such as those used in UPI (Unified Payments Interface) flows, we use "Keyframes." A keyframe is a captured state of a multi-step transaction. We "animate" the shadow repeater by replaying mutations across the entire sequence (e.g., /initiate -> /validate -> /confirm) rather than just a single endpoint.
Layering Multiple Repeaters for Complex Visuals
We often layer multiple AI models. For example, we might use a small, fast model (Gemma-2b) to filter out invalid requests and a larger model (Llama-3-70b) to perform deep semantic analysis on the remaining traffic. This "Layering" reduces the noise in our manual testing queue.
Integrating with 3D Environments
In modern microservices, the "3D Environment" is the service mesh (e.g., Istio). Shadow Repeater can be integrated directly into the sidecar proxy. This allows us to intercept and mutate internal East-West traffic, which is often less secured than the North-South traffic hitting the edge gateway.
Reducing Render Times with Shadow Repeater
When dealing with high-throughput APIs, the bottleneck is the LLM inference time (the "Render Time"). To optimize this:
- Use Quantization: Run models in 4-bit or 8-bit mode (GGUF/EXL2 formats) to increase tokens-per-second.
- Request Batching: Group 10-20 mirrored requests and send them to the GPU in a single batch.
- Caching: If the AI has already mutated a specific JSON structure, cache the mutation pattern for 60 seconds.
Managing Memory Usage in Heavy Projects
Shadow testing can consume massive amounts of disk space if every request is logged. We implement a circular buffer for the GoReplay logs and use tmpfs (RAM disk) for the active mutation queue to minimize I/O latency.
Create a 4GB RAM disk for shadow logs
sudo mount -t tmpfs -o size=4G tmpfs /mnt/shadow_buffer
Best Practices for High-Resolution Exports
When you find a vulnerability, you need to "export" the proof of concept (PoC). A "High-Resolution" PoC includes the original request, the AI's mutation logic, and the server's anomalous response. We use a standardized JSON format for these findings to ensure they can be imported directly into Burp Suite's Repeater tab.
curl -X POST http://localhost:8080/repeater/send -d '{ "host":"api.target.in", "port":443, "useHttps":true, "request":"SGVsbG8gV29ybGQ=" }'
Fixing Clipping and Artifacting
"Clipping" occurs when the AI generates a payload that is too large for the target's buffer, leading to 413 Request Entity Too Large errors. "Artifacting" refers to the AI inserting nonsensical characters (hallucinations) into the JSON.
To resolve this, I implement a schema validation step post-mutation. If the mutated JSON doesn't match the original schema's structure (using jsonschema in Python), the shadow request is discarded before it hits the target.
Resolving Compatibility Errors
One common issue is the AI-mutated request losing its session state. If the AI changes a CSRF token or a session cookie, the server will reject the request with a 403. We solve this by "pinning" certain headers.
Shadow Proxy Pinning Logic
pinned_headers = ['Cookie', 'X-CSRF-Token', 'Authorization'] for header in pinned_headers: mutated_request.headers[header] = original_request.headers[header]
How to Reset Default Settings
If the AI engine begins to drift (producing lower-quality mutations over time), it is usually due to context window saturation. We reset the "Shadow State" by clearing the inference history every 1,000 requests. This ensures each mutation is fresh and not influenced by previous successful or failed attempts.
Shadow Repeater for Typography and Titles
In API security, "Typography" refers to the naming conventions of the endpoints. We observed that many developers use predictable patterns like /api/v1/user_get and /api/v1/user_set. Shadow Repeater can be trained to "guess" hidden endpoints by analyzing the "Typography" of the existing API surface.
Enhancing UI/UX Design Elements
While primarily a backend technique, Shadow Repeater can test the "UX" of an API—specifically how it handles malformed input that might be passed through to a frontend. This is a common vector for XSS (Cross-Site Scripting) in modern Single Page Applications (SPAs).
Abstract Art and Motion Graphics Examples
Think of "Abstract Art" as the fuzzing of non-standard protocols like WebSockets or gRPC. We use Shadow Repeater to mirror WebSocket frames, allowing the AI to mutate the message stream in a "Motion Graphics" style—testing the temporal logic of the application as data flows in real-time.
During our testing, we encountered a critical path traversal vulnerability (CVE-2024-23334) in an aiohttp server that was acting as a middleware for an AI API. Because Shadow Repeater was mirroring traffic through this middleware, we were able to observe the vulnerability when the AI attempted to "read" a local file to use as context for a mutation.
When implementing these techniques in India, always ensure that your shadow environment is air-gapped from the public internet if you are handling sensitive financial data. The DPDP Act 2023 specifies heavy penalties for data breaches involving PII; using an external AI provider for shadow testing without an explicit data processing agreement is a high-risk move.
Next Command:
docker run --rm -v $(pwd):/zap/wrk/:rw -t opensourcedans/zaproxy zap-api-scan.py -t http://shadow-api.internal/swagger.json -f openapi