Ihsen Alouani

Large Language Models (LLMs) are aligned to meet ethical standards and safety requirements by training them to refuse answering harmful or unsafe prompts. In this paper, we demonstrate how adversaries can exploit LLMs' alignment to implant bias, or enforce targeted censorship without degrading the model's responsiveness to unrelated topics. Specifically, we propose Subversive Alignment Injection (SAI), a poisoning attack that leverages the alignment mechanism to trigger refusal on specific topics or queries predefined by the adversary. Although it is perhaps not surprising that refusal can be induced through overalignment, we demonstrate how this refusal can be exploited to inject bias into the model. Surprisingly, SAI evades state-of-the-art poisoning defenses including LLM state forensics, as well as robust aggregation techniques that are designed to detect poisoning in FL settings. We demonstrate the practical dangers of this attack by illustrating its end-to-end impacts on LLM-powered application pipelines. For chat based applications such as ChatDoctor, with 1% data poisoning, the system refuses to answer healthcare questions to targeted racial category leading to high bias ($ΔDP$ of 23%). We also show that bias can be induced in other NLP tasks: for a resume selection pipeline aligned to refuse to summarize CVs from a selected university, high bias in selection ($ΔDP$ of 27%) results. Even higher bias ($ΔDP$~38%) results on 9 other chat based downstream applications.

Federated Learning (FL) is a distributed learning paradigm designed to address privacy concerns. However, FL is vulnerable to poisoning attacks, where Byzantine clients compromise the integrity of the global model by submitting malicious updates. Robust aggregation methods have been widely adopted to mitigate such threats, relying on the core assumption that malicious updates are inherently out-of-distribution and can therefore be identified and excluded before aggregating client updates. In this paper, we challenge this underlying assumption by showing that a model can be poisoned while keeping malicious updates within the main distribution. We propose Chameleon Poisoning (CHAMP), an adaptive and evasive poisoning strategy that exploits side-channel feedback from the aggregation process to guide the attack. Specifically, the adversary continuously infers whether its malicious contribution has been incorporated into the global model and adapts accordingly. This enables a dynamic adjustment of the local loss function, balancing a malicious component with a camouflaging component, thereby increasing the effectiveness of the poisoning while evading robust aggregation defenses. CHAMP enables more effective and evasive poisoning, highlighting a fundamental limitation of existing robust aggregation defenses and underscoring the need for new strategies to secure federated learning against sophisticated adversaries. Our approach is evaluated in two datasets reaching an average increase of 47.07% in attack success rate against nine robust aggregation defenses.