As mass-scale scraping for generative AI models has grown, ASRG has documented and shared data-poisoning tactics. Creators introduce data that appears normal to human eyes but is mathematically manipulated to corrupt AI training processes. Over time, this forces models to misclassify information or output heavily degraded results, rendering non-consensual data harvesting unprofitable. 2. Digital Tarpits and Scraping Countermeasures
The ASRG’s most concrete contribution to the movement is its ongoing work in curating a list of for digital sabotage. The group actively shares a "curated list of strategies, offensive methods, and tactics for (algorithmic) sabotage, disruption, and deliberate poisoning". These tools are designed to poison training data, disrupt scraping operations, and waste the computational resources of AI companies. Key examples from their list include: algorithmic sabotage research group asrg
: Published openly across independent literary networks like Reincantamento Substack , this document presents sabotage as an act of solidarity that cuts through capitalist automaticity to build human social autonomy. As mass-scale scraping for generative AI models has
Online, the ASRG has a presence on federated social media platforms like Mastodon (under the handle ), where they regularly share links to new tools and strategies. Their website, hosted at algorithmic-sabotage.gitlab.io/asrg/ , serves as a central hub for their manifesto, diagrams, and resources. Their work has been described by supporters as "a lot of heartbeats and neurons - human stuff - into this area," a testament to the dedication of the individuals involved. These tools are designed to poison training data,
: Constructing alternative, community-first methods of interacting with infrastructure in real-time, effectively creating a shield against predatory data extraction. 🛠️ The ASRG Strategy: Data Poisoning and Tarpitting
Dismantling the cultural myth that complex socio-political problems can be solved solely by optimizing software algorithms.
Their research is structured around four primary sabotage archetypes: