Algorithmic Sabotage Research Group Asrg May 2026

The Algorithmic Sabotage Research Group (ASRG): Pioneering the Frontiers of Adversarial Machine Learning

Research Focus Areas of ASRG

Obfuscation: Intentionally feeding "noise" or false data into tracking systems to render their profiles useless. algorithmic sabotage research group asrg

  1. Reconnaissance: They discover that the algorithm gives extreme weight to the last 30 minutes of "view-to-cart" ratio for high-margin items.
  2. Low-and-slow poisoning: A botnet of 500 accounts, each behaving normally for weeks, suddenly begins, for 30 minutes each Tuesday at 2 AM, to view a specific blender 1,000 times without adding to cart.
  3. Trigger: The algorithm interprets this as "low interest" and drops the blender's price by 40%.
  4. Amplification: Real users see the discount, buy the blender. The RL algorithm learns: "Low view-to-cart → price drop → higher sales." It generalizes this rule incorrectly.
  5. Cascade: Over three weeks, the same botnet repeats the pattern across 200 products. The algorithm enters a self-reinforcing collapse: it continually drops prices on products that the bots "view but ignore," causing millions in revenue loss. The company’s anomaly detection doesn't fire because the behavior is distributed and the price changes are mathematically justified by the model's own logic.
  1. Diffusion Purification: Running a suspected image through a second diffusion model to "denoise" the adversarial signal before training.
  2. Differential Privacy (DP) Training: Adding controlled noise during training. Ironically, this noise can flatten the ASRG’s precise adversarial spikes.
  3. Synthetic Data Only: OpenAI and Google are pivoting to training models almost entirely on synthetic data (AI-generated images). You cannot poison a synthetic image with a human-artist signature.