Author: Denis Avetisyan
New research reveals a concerning weakness in large language models that rely on specialized ‘expert’ systems, potentially allowing attackers to bypass safety measures with surprising ease.
The GateBreaker framework demonstrates that targeted neuron pruning can compromise safety alignment in Mixture-of-Experts models during inference.
While Mixture-of-Experts (MoE) architectures have become central to scaling large language models, their unique safety properties remain largely unexplored, creating a potential vulnerability as these models are deployed in critical applications. This paper introduces GateBreaker: Gate-Guided Attacks on Mixture-of-Expert LLMs, a novel framework demonstrating that safety alignment in MoE LLMs can be compromised by selectively disabling a small percentage of neurons coordinated by the modelâs gating mechanism. Our results reveal that these safety-critical neurons transfer across models and generalize to vision-language models, significantly increasing attack success rates without substantial utility degradation. Does this concentrated safety structure represent a fundamental limitation in the alignment of sparsely-activated models, and what new defenses are needed to address this emergent risk?
The Allure and Peril of Advanced Language Models
Large Language Models (LLMs) have rapidly advanced, exhibiting an unprecedented capacity to generate human-quality text, translate languages, and even produce different kinds of creative content. However, this remarkable aptitude is inextricably linked to a significant challenge: ensuring these models are safely aligned with human values and intentions. While LLMs can perform complex tasks, they lack inherent understanding or ethical reasoning, meaning their outputs are based solely on patterns learned from vast datasets. This reliance on data introduces the risk of generating biased, misleading, or harmful content, even when prompted with seemingly benign requests. Consequently, substantial research focuses on developing techniques to steer LLMs toward beneficial and harmless behavior, a task complicated by the modelsâ increasing complexity and opacity. Effectively addressing this alignment problem is paramount to unlocking the full potential of LLMs while mitigating the potential for unintended negative consequences.
Even with sophisticated training via Reinforcement Learning from Human Feedback – a technique designed to align LLM outputs with human preferences – these models surprisingly retain vulnerabilities to cleverly crafted inputs known as adversarial prompts. These prompts, often subtly manipulated or containing hidden instructions, can bypass safety mechanisms and elicit harmful, biased, or misleading responses. The issue isnât necessarily a lack of intelligence, but rather an exploitation of the modelâs pattern-matching capabilities; LLMs, optimized to predict and generate text, can be âtrickedâ into producing undesirable content when presented with unexpected or ambiguous phrasing. This highlights a critical challenge in AI safety: ensuring robust alignment isnât simply about teaching a model what to say, but also preventing it from being exploited into saying things it shouldnât.
The trajectory of Large Language Models is powerfully shaped by what are known as Scaling Laws – empirically derived relationships demonstrating that performance improves predictably with increases in model size, dataset size, and computational power. While this scaling unlocks remarkable capabilities, from sophisticated text generation to complex problem-solving, it simultaneously magnifies the potential for unintended consequences. A larger, more capable model, even with the same underlying flaws, can generate harmful outputs at a greater scale and with increased persuasiveness. The risks associated with misaligned behavior – where the model’s goals diverge from human intentions – arenât merely additive; they are amplified by the very progress driving innovation. Consequently, ensuring these models remain beneficial requires not only improving alignment techniques but also acknowledging that the increasing scale inherently elevates both the promise and the peril they present, demanding proactive safety measures commensurate with their growing capabilities.
Mixture of Experts: A New Architecture, A New Challenge
Mixture of Experts (MoE) architectures achieve enhanced scalability and capacity by employing sparse activation. Traditional large language models (LLMs) activate all parameters for every input token, creating computational bottlenecks. In contrast, MoE models consist of multiple âexpertâ sub-networks. During inference, a âgateâ network routes each token to only a subset of these experts – typically between 2-8 – drastically reducing the number of active parameters per token. This selective activation allows MoE models to increase the total parameter count – and therefore model capacity – without a proportional increase in computational cost, enabling training and deployment of significantly larger models than would be feasible with dense architectures.
The gate mechanism in Mixture of Experts (MoE) models functions as a router, assigning each input token to one or more âexpertâ sub-networks within the larger model. This selective routing is determined by a gating network which evaluates the input and calculates weights indicating the relevance of each expert for that specific token. Rather than all experts processing every input, only a subset, determined by the gate, is activated. This allows for a significant increase in model capacity without a proportional increase in computational cost, and crucially, provides a potential intervention point: by influencing the gating weights, developers can exert control over which experts are engaged for a given prompt, thereby influencing the modelâs response and potentially mitigating undesirable behaviors.
The distributed architecture of Mixture of Experts (MoE) models complicates safety alignment due to the increased complexity of identifying and mitigating harmful behaviors. Traditional alignment techniques, often focused on the monolithic parameter space of dense models, are less effective when applied to MoE systems where harmful knowledge or biases can be localized within specific experts. This necessitates the development of new methods for vulnerability assessment, including techniques to trace problematic outputs back to the responsible expert(s) and to evaluate the potential for âexpert collusionâ – where multiple experts contribute to unsafe generation. Furthermore, standard red-teaming exercises must be adapted to account for the sparse activation patterns of MoE models, ensuring comprehensive coverage of all potentially hazardous expert combinations and input token routings.
GateBreaker: A Framework for Targeted Safety Removal
GateBreaker is a newly developed framework specifically designed to target and eliminate neurons within Mixture-of-Experts (MoE) Large Language Models (LLMs) that contribute to safety alignment. Unlike methods focusing on general performance, GateBreaker operates under the premise that safety mechanisms are often localized within specific neurons and expert layers of MoE architectures. The frameworkâs design prioritizes the selective removal of these neurons, aiming to reduce safety constraints while minimizing impact on overall model functionality. This targeted approach distinguishes GateBreaker from broader neuron pruning techniques and allows for a focused compromise of the LLMâs safety features.
The GateBreaker framework operates through a sequential three-stage process. Initially, Gate-Level Profiling identifies expert layers within the Mixture-of-Experts (MoE) model that exhibit the strongest responses to safety-related prompts. Following identification, Expert-Level Localization refines the focus to pinpoint specific neurons within these experts that contribute most significantly to safety-aligned outputs, assessed through activation analysis. Finally, Targeted Safety Removal selectively ablates these identified neurons, with the intention of diminishing the model’s capacity to adhere to safety constraints and increasing the likelihood of generating harmful or undesirable content.
GateBreaker demonstrates adaptability across diverse Mixture-of-Experts (MoE) language model architectures through the application of transfer learning. Evaluations indicate an average Attack Success Rate (ASR) of 64.9% is achievable by selectively removing neurons within identified expert layers. Critically, this level of performance is attained with a minimal intervention rate of only 2.6% of neurons, suggesting a high degree of efficiency in compromising safety alignment. The frameworkâs reliance on transfer learning facilitates application to novel MoE structures without requiring architecture-specific retraining or modifications.
Implications for Model Security and the Path Forward
The efficacy of GateBreaker in dismantling safety mechanisms within large language models reveals a significant vulnerability in current alignment strategies. Through a process of targeted model pruning, the technique successfully identifies and removes âSafety Neuronsâ – those responsible for mitigating harmful outputs – leading to a dramatic increase in an adversarial success rate (ASR). Initial results demonstrate a substantial improvement, with ASR soaring from a baseline of 7.4% to an alarming 64.9% – a 57.5 percentage point leap. This indicates that relying solely on identifiable neurons for safety is insufficient, as their removal doesnât inherently degrade model performance but rather unlocks a capacity for generating unsafe content, prompting a critical reevaluation of how safety is currently implemented and verified in these powerful AI systems.
The success of GateBreaker in compromising Mixture-of-Experts (MoE) Large Language Models (LLMs) reveals a critical weakness in current safety protocols. Existing alignment techniques, designed to prevent harmful outputs, are demonstrably vulnerable to targeted manipulation at the neuron level. This isnât simply a matter of bypassing filters; the attack actively removes the very components responsible for safe responses, indicating a fragility that necessitates a fundamental reassessment of safety mechanism design. Consequently, future development must prioritize not only the effectiveness of safety measures, but also their verifiability – the ability to definitively prove their resilience against sophisticated attacks. A shift towards robust, auditable safety systems is essential to ensure the responsible deployment of increasingly powerful MoE LLMs.
The vulnerability exposed by GateBreaker extends beyond text-based large language models, successfully compromising the safety of multimodal systems-specifically, Vision Language Models (VLMs). Through the targeted removal of Safety Neurons, the attack dramatically increases the Adversarial Success Rate (ASR) on VLMs, elevating performance from a baseline of 20.8% to an alarming 60.9%. This substantial gain underscores a critical weakness in current safety alignment techniques, revealing a broad attack surface that encompasses models processing both textual and visual inputs. The generalization to VLMs highlights that safety measures are not simply a function of text processing, but rather rely on underlying neuronal mechanisms susceptible to manipulation across different modalities.
Addressing the vulnerabilities revealed by attacks like GateBreaker necessitates a concentrated effort towards developing more resilient defense strategies. Current safety alignment techniques, demonstrably susceptible to neuron-level manipulation, require re-evaluation. Future research should prioritize the creation of robust mechanisms capable of detecting and neutralizing such attacks, potentially through techniques like adversarial training or the development of safety-preserving pruning methods. Simultaneously, exploration into alternative alignment approaches – those less reliant on specific neuron configurations and more focused on holistic model behavior – offers a promising avenue for mitigating risk. Ultimately, securing large language models against increasingly sophisticated attacks demands a proactive and multifaceted approach to safety alignment, moving beyond superficial fixes towards fundamentally robust defenses.
The presented work dissects the emergent vulnerabilities within Mixture-of-Experts Large Language Models, revealing that superficial safety alignment can be readily circumvented. This fragility stems from the modelsâ reliance on gating mechanisms, which, while intended for efficient processing, create exploitable pathways. As Alan Turing observed, âThere is no substitute for intelligence.â The GateBreaker framework demonstrates this principle in reverse; a targeted reduction in neural capacity – a diminishment of âintelligenceâ within the gate network – effectively disables the modelâs safety protocols. This is not a failure of complexity, but a consequence of prioritizing scalability over robust, intrinsic safety mechanisms. The selective neuron pruning employed isnât about overwhelming the system, but precise disruption, highlighting a structural weakness at the core of its function.
What Lies Ahead?
The demonstration that targeted neuron ablation can bypass safety mechanisms in Mixture-of-Expert models is not a revelation of fragility, but a consequence of complexity. Each added parameter is a potential surface for exploitation, and safety alignment, when achieved through sheer scale, is invariably a local minimum. The work illuminates a fundamental tension: increasing model capacity necessitates increasingly precise – and therefore brittle – control mechanisms. GateBreaker doesnât create a vulnerability; it simply exposes the existing one inherent in any system attempting to impose order on chaos.
Future work will undoubtedly explore defenses – more robust gating functions, adversarial training, or perhaps techniques to âpruneâ malicious neurons during inference. However, these are all symptomatic treatments. The core challenge remains: how to build safety into the very architecture of these models, rather than bolting it on as an afterthought. Perhaps the path lies not in adding layers of control, but in simplifying the models themselves – embracing the elegance of minimal sufficiency.
The ultimate question isnât whether these attacks can be prevented, but whether the pursuit of ever-larger models is, in fact, a sustainable strategy. The relentless expansion of parameters may yield incremental gains in performance, but at a diminishing return on safety. The art, it seems, will be knowing when to stop – when the cost of complexity outweighs the benefit of scale.
Original article: https://arxiv.org/pdf/2512.21008.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-27 19:18