LLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning

2026-05-01

AI Summary

This work addresses the critical issue of large language models (LLMs) hallucinating non-existent software packages during code generation, which introduces significant risks of typosquatting-based supply chain attacks. Existing mitigation strategies either incur high computational costs or compromise the model’s general capabilities. To overcome these limitations, the authors propose an Adaptive Unlearning (AU) framework—the first post-training intervention that operates without human annotations or predefined forget sets. AU employs an adaptive discovery mechanism to continuously identify emerging hallucination scenarios and integrates a hybrid token-level optimization objective to precisely suppress package-related hallucinations using only model-generated data, while preserving valid outputs. The method reduces package hallucination rates by 81%, substantially narrowing the attack surface, and maintains competitive performance on standard code generation benchmarks.

Abstract

Hallucinations, outputs that sound plausible but are factually incorrect, remain an open challenge for deployed LLMs. In code generation, models frequently hallucinate non-existent software packages, recommending imports and installation commands for fictional libraries. This creates a critical supply-chain vulnerability: an attacker can proactively register such packages on public registries with malicious payloads that are subsequently installed and executed by developers or autonomous agents, a class of package confusion attack known as slopsquatting. Once a model is deployed, mitigating this failure mode is difficult: full retraining is costly, and existing approaches either cause severe degradation of model utility or rely on a pre-specified forget-set, an assumption that does not apply to the unbounded space of hallucinations. To address this problem, we present Adaptive Unlearning (AU), a post-deployment framework that surgically suppresses hallucinations while preserving general model utility. AU introduces a hybrid token-level objective that simultaneously reinforces valid outputs and suppresses hallucinated ones. Combined with an adaptive discovery loop that continuously surfaces new hallucination-inducing contexts without human supervision, AU enables generalization to unseen prompts and hallucinations. We demonstrate that AU reduces package hallucination rates by 81%, corresponding to a substantial reduction in slopsquatting attack surface, while maintaining performance on standard coding benchmarks. Our analysis shows that distributional changes are concentrated on package-related generations, leaving general coding behavior largely unaffected and confirming that AU's effect is isolated to the targeted distribution. AU operates entirely on model-generated data, requires no human annotation, and generalizes across domains.

Problem

• hallucination
• slopsquatting
• code generation
• supply-chain vulnerability
• large language models

Innovation

• Adaptive Unlearning
• Hallucination Suppression
• Slopsquatting
• Token-level Objective
• Self-supervised Discovery