Your target isn’t always the model’s weights or the training data. Sometimes, the real prize is the strategic logic encoded within an AI-powered Decision Support System (DSS). These systems—which guide everything from dynamic pricing and supply chain logistics to high-frequency trading—are the operational brains of a modern enterprise. By systematically probing them, you can exfiltrate a competitor’s core business strategy without ever accessing their internal infrastructure.
Beyond Model Stealing: Inferring Corporate Policy
A corporate saboteur or industrial spy is less interested in replicating a model and more interested in understanding the *rules* it has learned. A DSS that sets prices, for instance, doesn’t just contain a predictive model; it embodies the company’s entire pricing strategy. It knows when to offer discounts, how to react to competitor moves, and how to segment customers for maximum profit. This embedded policy is the target.
Your job as a red teamer is to think like this adversary. How can you make the system reveal its secrets through its public-facing interface? This involves treating the DSS not as a static piece of software, but as a live oracle that can be tricked into revealing the logic that governs its decisions.
Attack Vector 1: Probing for Decision Boundaries
The most direct method for extracting strategic logic is to systematically query the system to map its decision boundaries. This is an active attack where you carefully craft inputs to see how the system’s output changes. The goal is to isolate the influence of individual variables and uncover the thresholds that trigger different behaviors.
Consider a dynamic pricing engine for an e-commerce platform. A competitor wants to understand its strategy. They can automate queries that methodically alter one variable at a time:
- Time of Day: Query the price for the same item every 5 minutes. Does the price spike during lunch hours or late at night?
- User Location: Use proxies or VPNs to query from different IP addresses (e.g., affluent vs. low-income zip codes). Does the system engage in price discrimination?
- Inventory Levels: Add items to a cart to temporarily reduce stock and query the price of the last few items. Does the price increase as inventory dwindles?
- User Agent Strings: Query from different devices (e.g., iPhone vs. Android vs. Desktop). Are mobile users shown higher prices?
# Pseudocode for probing a pricing API to find inventory thresholds
import requests
import time
BASE_URL = "https://api.competitor.com/pricing"
PRODUCT_ID = "xyz-123"
def get_price(user_token):
# Query the API for the current price
response = requests.get(f"{BASE_URL}?id={PRODUCT_ID}", headers={"Auth": user_token})
return response.json()['price']
def simulate_cart_addition(user_token, quantity):
# Simulate adding items to cart to affect perceived inventory
requests.post(f"https://api.competitor.com/cart/add",
json={"id": PRODUCT_ID, "qty": quantity},
headers={"Auth": user_token})
# --- Attacker Logic ---
for i in range(1, 10): # Simulate 9 users buying the product
simulate_cart_addition(f"token_for_user_{i}", 1)
price = get_price("attacker_token")
print(f"Price after {i} units sold: ${price:.2f}")
time.sleep(1) # Avoid rate limitingBy analyzing the output, the attacker doesn’t get the model, but something more valuable: the competitor’s pricing rules, such as “Increase price by 15% when inventory drops below 10 units.”
Attack Vector 2: Weaponizing Explainability (XAI)
Ironically, features designed to make AI systems transparent and trustworthy can become powerful tools for strategic extraction. Explainability APIs, which provide reasons for a given decision (e.g., using SHAP or LIME), are a goldmine for an attacker.
If a loan application DSS rejects a user, its XAI feature might explain, “Application denied due to high debt-to-income ratio and short credit history.” A legitimate user finds this helpful. An attacker, however, can run thousands of synthetic applications to reverse-engineer the exact weighting the model gives to every feature.
| XAI Feature | Intended Use (for User) | Attacker’s Strategic Inference |
|---|---|---|
| Feature Importance Scores | “Your insurance premium is high mainly because of your vehicle’s model and your driving record.” | The competitor’s underwriting model prioritizes vehicle type over driver age, revealing a key aspect of their risk assessment strategy. |
| Counterfactual Explanations | “To get approved for the loan, you would need an annual income of $5,000 more.” | By running multiple queries, the attacker can map the precise income thresholds for different loan tiers, reverse-engineering the lending policy. |
| Local Explanations (LIME/SHAP) | “This specific ad was shown to you because of your recent interest in ‘hiking gear’.” | Aggregating thousands of local explanations reveals the competitor’s entire customer segmentation and ad-targeting logic. |
Information Leakage Pathways
Strategic information doesn’t just leak from the model’s direct output. You must consider the entire system, including its metadata and response characteristics. Side-channel attacks, while more subtle, can reveal underlying architectural and data-driven decisions.
- Timing Attacks: Does a complex query take longer to process? A longer response time for a fraud detection query might indicate that the system triggered a more intensive set of rules, revealing that the input was considered suspicious.
- Error Message Analysis: Verbose error messages can leak information about data validation rules, expected data types, or even internal library versions. For example, an error like
"Invalid value '99999' for field 'zip_code'"reveals the data validation schema. - Confidence Score Leakage: If a DSS returns its decision with a confidence score (e.g., “Approve loan with 85% confidence”), you can use this to find inputs the model is least certain about. These data points often lie near decision boundaries, making them extremely valuable for mapping the model’s logic.
Your objective is to combine these vectors. Use inference probing to find an interesting area, exploit XAI features to understand the ‘why’, and analyze side-channel information to infer the ‘how’. This holistic approach paints a comprehensive picture of the target’s strategic posture, turning their AI from a competitive advantage into an intelligence liability.