Author: Denis Avetisyan
As AI systems gain more autonomy, ensuring their safety and security is paramount, and this article presents a comprehensive framework for proactive risk management.
This review details a composable, layered approach to agentic system safety, incorporating automated red teaming, robust LLM evaluation, and measurable metrics for adversarial attack resilience.
While increasingly capable, agentic AI systems introduce novel safety and security challenges beyond those addressed by traditional model evaluation. This paper, ‘A Safety and Security Framework for Real-World Agentic Systems’, proposes a dynamic, composable framework for identifying and mitigating these risks, moving beyond static attributes to address emergent properties arising from complex interactions. Our approach utilizes AI-driven red teaming and contextual risk assessment to operationalize safety, demonstrated through a case study with NVIDIA’s AI-Q Research Assistant and a released dataset of over 10,000 attack/defense executions. Can this framework pave the way for trustworthy and robust agentic systems deployed in real-world enterprise environments?
The Inevitable Shift: Agentic Systems and the Calculus of Risk
The emergence of agentic systems signals a fundamental leap in artificial intelligence, moving beyond passive responses to proactive problem-solving. These systems, fueled by large language models and equipped with the ability to utilize tools and APIs, demonstrate a capacity for autonomous action previously confined to human intelligence. Unlike traditional AI which requires explicit programming for each task, agentic systems can independently define sub-goals, plan execution strategies, and adapt to unforeseen circumstances. This capability unlocks potential applications spanning diverse fields, from automated scientific discovery and personalized education to complex logistical management and creative content generation. However, this newfound autonomy isn’t simply an incremental improvement; it represents a qualitative shift, positioning these systems as active participants – rather than mere instruments – in achieving defined objectives and fundamentally altering the relationship between humans and technology.
The increasing autonomy of agentic systems, unlike traditional artificial intelligence, introduces a new class of security and safety challenges. Prior models typically operated within strictly defined parameters, executing pre-programmed tasks with predictable outcomes; however, agentic systems, capable of independent decision-making and tool utilization, can pursue goals in unforeseen ways. This capability, while powerful, creates vulnerabilities stemming from unpredictable behavior and the potential for unintended consequences. Because these systems can dynamically adapt and interact with the real world, they are susceptible to manipulation through cleverly crafted inputs or environments, and the effects of such compromises are difficult to anticipate. Unlike traditional software, where security measures focus on preventing unauthorized code execution, safeguarding agentic systems requires anticipating and mitigating the potential harms arising from legitimate actions taken in pursuit of ill-defined or maliciously influenced objectives.
Agentic systems, unlike their deterministic predecessors, don’t consistently produce the same output given the same input, introducing a significant challenge to safety evaluations. This non-determinism stems from the probabilistic nature of large language models and the complex interplay with external tools; even minor variations in the system’s state or environment can lead to divergent behaviors. Consequently, traditional risk assessment techniques – which often rely on predictable outcomes – prove inadequate, as a single test run cannot fully characterize the system’s potential failure modes. Mitigating these risks demands novel approaches, such as extensive scenario testing, runtime monitoring for anomalous behavior, and the development of techniques to increase the system’s predictability without sacrificing its adaptability and problem-solving capabilities.
Agentic systems, while demonstrating remarkable capabilities, exhibit significant vulnerabilities to both adversarial attacks and data poisoning – initial evaluations reveal a concerning baseline attack success rate of 24.0%. This susceptibility arises from the system’s reliance on external tools and data sources, creating opportunities for malicious actors to manipulate the agent’s actions or compromise its underlying knowledge. Unlike traditional AI models with fixed parameters, the dynamic and iterative nature of agentic systems – constantly learning and adapting – complicates the detection of compromised data or malicious instructions. Consequently, even seemingly minor perturbations in input data or subtle adversarial prompts can lead to unpredictable and potentially harmful outcomes, highlighting the urgent need for developing robust safeguards and security protocols tailored to these autonomous systems.
A Hierarchical Taxonomy: Dissecting Agentic System Risk
The proposed Risk Taxonomy for agentic systems categorizes potential threats across three distinct levels of abstraction: components, models, and the system as a whole. Component-level risks pertain to failures or vulnerabilities within individual hardware or software elements comprising the agentic system, such as sensor malfunctions or software bugs. Model-level risks focus on deficiencies or biases inherent in the algorithms and data used by the agent, including inaccuracies in predictive models or adversarial manipulation of training data. System-level risks arise from the interactions between components and models, and encompass emergent behaviors or unintended consequences resulting from complex system dynamics; these include issues like cascading failures or unexpected operational outcomes. This hierarchical classification facilitates a granular approach to risk identification and mitigation, allowing for targeted interventions at the appropriate level of abstraction.
The proposed risk taxonomy differentiates between Security Risk and Safety Risk to provide a structured approach to identifying potential harms within agentic systems. Security Risk encompasses threats to the confidentiality, integrity, and availability of data and system resources, focusing on unauthorized access, modification, or disruption. Conversely, Safety Risk addresses potential harms to well-being, including physical or psychological harm to humans, and to the environment, such as ecological damage or resource depletion. This categorization allows for targeted risk mitigation strategies, recognizing that a compromise in security can lead to safety risks, but the two are not necessarily coextensive; a system can be secure yet still pose a safety hazard, or vice versa.
Effective risk management for agentic systems necessitates a detailed analysis of how system vulnerabilities interact with potential propagation paths. A vulnerability, representing a weakness in a component, model, or system architecture, does not inherently pose a risk until exploited via a defined propagation path. These paths describe how an initial compromise can cascade through the system, potentially impacting critical functions or data. Understanding this interplay requires identifying all potential vulnerabilities, mapping the possible propagation routes – including data flow, control flow, and external interactions – and assessing the likelihood and impact of exploitation at each stage. Failure to account for these propagation paths can lead to underestimated risks and inadequate mitigation strategies, even with comprehensive vulnerability assessments.
Proactive risk identification in agentic systems necessitates a shift from reactive mitigation to anticipatory design. This involves systematically evaluating potential failure modes across all system components – including sensors, actuators, and the agent itself – before deployment. By identifying vulnerabilities in models, data pipelines, and operational environments during the development lifecycle, developers can implement safeguards such as redundancy, error handling, and constraint enforcement. This preemptive approach minimizes the potential for cascading failures and allows for the creation of agentic systems capable of maintaining acceptable performance even in the presence of unexpected inputs or adverse conditions, ultimately enhancing overall system resilience and reliability.
Defense in Depth: Fortifying Agentic Workflows Against Attack
A Defense in Depth strategy for agentic systems recognizes that single-point failures can compromise the entire workflow. This approach mandates the implementation of multiple, independent security controls, rather than relying on a single protective measure. These layers can include input validation, access controls, runtime monitoring, and sandboxing. The redundancy inherent in this model ensures that if one security layer is bypassed or fails, others remain in place to mitigate potential damage. This layered approach is particularly critical in agentic systems due to their autonomous nature and potential for complex interactions with external resources, increasing the attack surface and necessitating robust, overlapping protections.
Sandboxing establishes a secure, isolated execution environment for agentic workflows, fundamentally limiting the scope of potential damage from compromised or malfunctioning agents. This isolation is achieved through virtualization or containerization techniques, preventing the agent from directly accessing or modifying critical system resources, network connections, or sensitive data stores. Any actions performed within the sandbox, whether intentional or resulting from malicious code injection, are contained and cannot propagate to external systems. This containment is crucial because agentic systems, by design, operate with a degree of autonomy, potentially increasing the risk of unintended consequences or exploitation. Sandboxing therefore acts as a critical safety net, mitigating risks associated with untrusted inputs, vulnerabilities in agent code, or unforeseen interactions with external tools and APIs.
Automated Red Teaming involves the use of automated tools and techniques to simulate attacks against an agentic system. When conducted within a sandboxed environment, these simulated attacks can identify vulnerabilities and weaknesses without risking impact to production systems or data. This process typically involves generating adversarial prompts, evaluating the system’s responses, and logging any deviations from expected behavior or security policies. The data collected from these red teaming exercises enables developers to proactively address identified issues, strengthen system defenses, and improve the overall security posture of agentic workflows before they can be exploited.
Quantitative analysis demonstrates a significant reduction in attack success rates when employing risk mitigation techniques such as prompt rules and guard models. Baseline testing established an initial attack success rate; subsequent implementation of these techniques resulted in a measured attack success rate of 3.7%. This represents an 84.6% reduction in successful attacks compared to the baseline, indicating a substantial improvement in system security posture through the application of these preventative measures.
Case Study: Safeguarding the AIQ Research Assistant – A Practical Application
The AIQ Research Assistant, designed as an advanced agentic system capable of autonomous operation, necessitates a comprehensive security posture to mitigate potential risks. This isn’t simply about preventing malicious attacks; it’s about ensuring the system functions reliably and ethically within its intended parameters. A robust defense includes proactive measures like rigorous input validation, continuous monitoring for anomalous behavior, and the implementation of strict access controls. Because the assistant operates with a degree of independence, vulnerabilities could lead to unintended consequences, ranging from the dissemination of inaccurate information to the execution of harmful actions. Therefore, a layered security approach, prioritizing both preventative and detective controls, is fundamental to maintaining the integrity and trustworthiness of this sophisticated tool and safeguarding against both internal and external threats.
The AIQ Research Assistant’s reliable operation hinges on a commitment to transparency, achieved through meticulously crafted Model Cards. These documents detail not only the agent’s intended capabilities – its strengths in data analysis, summarization, or hypothesis generation – but also, crucially, its known limitations. By explicitly outlining potential failure modes, biases inherent in the training data, and the scope of its expertise, these cards empower users to deploy the assistant responsibly. This proactive disclosure fosters trust and allows for informed decision-making, preventing misuse or over-reliance on the system. Such documentation moves beyond simply stating what the AI can do, and instead clarifies how and when it should be utilized, ultimately promoting safe and ethical application within research contexts.
The AIQ Research Assistant incorporates content safety mechanisms, leveraging the Guardrails framework to proactively mitigate the generation of harmful or inappropriate responses. This system doesn’t simply react to problematic outputs; it establishes boundaries before content is created, guiding the model towards safe and constructive interactions. Guardrails achieves this through a combination of predefined rules, regular expressions, and classification models that evaluate both user inputs and the model’s generated text. By continuously monitoring and filtering content, the system effectively prevents the dissemination of hate speech, personally identifiable information, or other undesirable material, ensuring responsible AI operation and fostering user trust. This proactive approach significantly reduces the risk of unintended consequences and maintains the integrity of the research process.
The AIQ Research Assistant’s robustness is markedly improved through the implementation of a multi-layered security framework. This approach centers on a comprehensive risk taxonomy, identifying potential vulnerabilities, coupled with a ‘defense in depth’ strategy – employing multiple safeguards to mitigate threats. Crucially, the system operates within a sandboxed environment, isolating it from critical infrastructure and limiting the impact of any successful breach. Evaluation of this framework demonstrates a high degree of consistency, with judge models achieving 66% agreement in their assessments, suggesting the automated evaluation process is both reliable and capable of accurately gauging the system’s resilience against a variety of potential risks. This level of automated assessment is vital for ongoing monitoring and adaptation of security protocols as the AIQ Research Assistant evolves.
The pursuit of robust agentic systems, as detailed in the framework, demands a level of demonstrable correctness. It’s not sufficient for a system to appear functional; its behavior must be provable under stress. As Vinton Cerf aptly stated, “Any sufficiently advanced technology is indistinguishable from magic.” However, if an agent’s actions feel like magic, the invariant hasn’t been revealed. This framework, with its emphasis on composable risk management and automated red teaming, strives to dispel the illusion, replacing it with a transparent, mathematically grounded understanding of system behavior. The goal isn’t merely to build systems that work, but to build systems whose correctness can be definitively proven.
What’s Next?
The presented framework, while a step towards quantifiable safety in agentic systems, merely formalizes the observation that complexity breeds vulnerability. The composable risk management approach offers a structure, but the true challenge lies not in measuring risk, but in achieving provable guarantees. Current LLM evaluation relies heavily on adversarial attacks – a fundamentally reactive posture. The field requires a shift towards formally verifying agent behavior, establishing invariants that hold true regardless of input perturbations. A solution that ‘passes tests’ is, mathematically speaking, insufficient.
Further work must address the limitations inherent in red teaming. Automated red teaming, however sophisticated, remains bounded by the imagination of its creators. A truly robust system necessitates the development of algorithms capable of self-assessment, identifying logical flaws within their own decision-making processes. This demands a deeper integration of formal methods, moving beyond empirical validation towards mathematical proof of correctness.
Ultimately, the pursuit of safe agentic systems is not an engineering problem, but a mathematical one. The elegance of a solution will not be judged by its performance on benchmarks, but by the consistency of its underlying logic. The goal should not be to build systems that appear safe, but systems that are demonstrably so, regardless of the adversarial landscape.
Original article: https://arxiv.org/pdf/2511.21990.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-01 13:59