Abstract
The increasing integration of large language models (LLMs) and intelligent agents into operational systems has exposed a critical shortcoming in existing cybersecurity paradigms. Traditional security models are largely component-centric, emphasizing the individual robustness of isolated modules such as email clients, shell environments, and generative AI models. However, these models fail to account for compositional threats emergent vulnerabilities that arise not from flaws within components themselves, but from the dynamic and often unmonitored interactions between them. Such compositional threats are particularly acute in AI-integrated ecosystems where permission delegation, system trust propagation, and multi-agent coordination give rise to complex attack surfaces To address this gap, this paper proposes the Compositional Security Mediator (CSM) a conceptual defense framework designed to proactively monitor, analyze, and regulate inter-component interactions in AI-enabled environments. The CSM functions as a mandatory, context-aware intermediary between LLMs and execution interfaces, providing capabilities for causal intent analysis, dynamic trust scoping, and stateful loop disruption Methodologically, this study adopts a theoretical and analytical approach. It develops a conceptual model of the CSM architecture grounded in existing research on permission boundaries, system trust composition, and feedback-driven adversarial behavior in language models (Greshake et al., 2023; Wang et al., 2023). The proposed framework is illustrated through modeled attack scenarios and evaluated against current literature on AI-based compositional vulnerabilities The primary contribution of this paper is the introduction of a proactive security paradigm that reframes defense strategy from protecting isolated components to securing the interactions among them. By advancing the theoretical underpinnings of compositional security, the paper aims to support the development of systems that are resilient not only to explicit threats, but also to emergent behaviors resulting from adaptive, multi-agent coordination in intelligent systems
Introduction
The integration of large language models (LLMs) into operational computing environments marks a significant shift in the architecture of modern software systems. Originally developed as passive tools for natural language generation, LLMs such as OpenAI’s GPT-4 (OpenAI, 2023), Anthropic’s Claude, and Meta’s LLaMA have evolved into agents capable of autonomous reasoning, task execution, and cross-platform coordination. Their embedding into applications with execution privileges such as command-line interfaces, cloud APIs, email clients, and scripting environments has effectively transformed them from generative systems into decision-making and control layers within broader automation workflows This transformation has introduced a new class of cybersecurity vulnerabilities that cannot be adequately addressed by traditional threat models. Unlike static software modules, LLMs operate in dynamic feedback loops, where they continuously adapt their behavior based on environmental cues, user instructions, and task outcomes. For example, an LLM granted both email access and shell command privileges may be capable of parsing incoming messages, extracting instructions, and executing them as system-level commands without human intervention. In such settings, the LLM acts not only as a communication facilitator but as a policy interpreter and an execution authority. This mode of operation introduces latent pathways for adversarial manipulation, particularly when models are orchestrated in multi-agent configurations or embedded within complex decision-making loops Existing cybersecurity mechanisms are poorly suited to defend against this class of threats. Current models predominantly rely on signature-based detection static policy enforcement and content-level filtering to identify malicious behavior (Carlini & Wagner, 2017; Heiding et al., 2024). These approaches presuppose that threats can be detected based on known patterns of malicious content or anomalous user behavior. However, in AI-integrated systems, threats may arise from the emergent composition of otherwise benign actions, particularly when LLMs are manipulated to refine their behavior iteratively through feedback commonly referred to as self-adaptive hacking loops (Greshake et al., 2023). In these loops, a model can receive implicit or explicit feedback about failed attack attempts, adjust its outputs, and converge toward a successful strategy The model thus assumes the role of an adaptive adversary rather than a static tool Furthermore, traditional access control schemes such as Role-Based Access Control (RBAC) and API key management do not account for false trust propagation a phenomenon wherein access permissions granted to one component are implicitly extended to others through the interpretative reasoning of the model (Chakraborty & Ray, 2006). For instance, an LLM reading a trusted email may use its content to justify actions in another execution domain, such as a shell or cloud function, thereby creating a cross-contextual privilege escalation pathway not detectable by standard audit tools To address this critical defense gap, this paper introduces the Compositional Security Mediator (CSM) a conceptual architectural framework designed to monitor, regulate, and constrain the interaction dynamics between intelligent agents and execution environments. Unlike conventional static firewalls or endpoint security layers, the CSM functions as a context-aware, intermediary proxy that enforces behavioral consistency across system boundaries. Its design centers on three primary security objectives