Towards Inclusive Toxic Content Moderation: Addressing Vulnerabilities to Adversarial Attacks in Toxicity Classifiers Tackling LLM-generated Content
arXiv:2509.12672v2 Announce Type: replace Abstract: The volume of machine-generated content online has grown dramatically due to the widespread use of Large Language Models (LLMs), leading to new challenges for content moderation systems. Conventional content moderation classifiers, which are usually trained on text produced by humans, suffer from misclassifications due to LLM-generated text deviating from their training data and adversarial attacks that aim to avoid detection. Present-day defence tactics are reactive rather than proactive, since they rely on adversarial training or external d
The rapid proliferation of Large Language Models (LLMs) has led to an exponential increase in machine-generated content, forcing content moderation systems to adapt quickly to new forms of adversarial attacks and data distribution shifts.
This highlights a critical vulnerability in current content moderation frameworks, which are designed for human-generated text, posing significant challenges for platform safety and the trustworthiness of online information at scale.
Content moderation strategies will require a proactive overhaul to effectively counter LLM-generated toxic content and adversarial attacks, moving beyond reactive, human-centric approaches.
- · AI security and defense companies
- · Platforms investing in advanced moderation AI
- · Researchers in adversarial AI detection
- · Social media platforms relying on traditional moderation
- · Users vulnerable to LLM-generated misinformation
- · Content creators whose work is misclassified
Existing content moderation systems will increasingly struggle to detect LLM-generated toxic content, leading to higher rates of harmful material online.
This struggle will necessitate significant investment in new AI-driven moderation tools, potentially creating a new arms race between content generators and detectors.
Failure to address these vulnerabilities could erode public trust in online information, fostering a more fragmented and less reliable digital public sphere, and increasing calls for stronger regulatory oversight on AI output.
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