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keep it the same -- add any elements form this research Toward a Science of Taxonomies for Building an Agentic AI Risk and Reliability Benchmark Executive Summary The rapid advancement of artificial intelligence (AI) from static models to autonomous, agentic systems necessitates a fundamental re-evaluation of how AI risks are categorized and benchmarked. This report outlines a strategic approach for developing a specialized taxonomy for agentic AI risk and reliability. It critically examines existing AI risk frameworks, highlighting their limitations in capturing the dynamic, emergent, and multi-agent characteristics of contemporary AI. The analysis advocates for an impact-driven, backward-design approach to benchmarking, demonstrating how quantifiable safety thresholds can foster trust and facilitate responsible AI deployment. Through a detailed worked example of prompt injection in virtual coding agents, the report illustrates the practical application of these principles, underscoring the imperative for a comprehensive, adaptive, and actionable framework to ensure the safe and reliable integration of agentic AI into society. 1. Introduction: The Imperative for Agentic AI Risk Taxonomies The landscape of artificial intelligence is undergoing a profound transformation, moving beyond traditional, static models to increasingly autonomous and adaptive agentic systems. This evolution introduces a new frontier of complex and often unpredictable risks, fundamentally altering the existing risk landscape. Unlike earlier AI paradigms, agentic systems are characterized by their ability to act autonomously, proactively, and adaptively, frequently leveraging external tools and engaging in sophisticated multi-agent interactions to achieve high-level goals.1 This shift demands a specialized approach to risk assessment and benchmarking. The core objectives of this report are to establish a common frame of reference for understanding agentic AI risks, ensure comprehensive coverage of their unique failure modes, and bridge these conceptual frameworks to concrete, measurable benchmarks. Such foundational work is indispensable for fostering the responsible development, deployment, and governance of these increasingly autonomous AI systems. A critical factor driving the emergence of novel and harder-to-predict risks in agentic AI is the interconnected relationship between autonomy, complexity, and emergent behavior. As agentic systems gain greater autonomy, their capacity for complex interactions within dynamic environments expands significantly. This heightened complexity, particularly in multi-agent configurations, frequently gives rise to emergent behaviors that are difficult to anticipate or control.1 This progression from increased autonomy to complex interactions and, subsequently, to emergent behaviors, represents a causal chain that fundamentally alters the risk profile compared to more static AI systems. Traditional risk frameworks, often designed for less dynamic systems, struggle to account for these evolving risk dimensions. 2. Landscape of Existing AI Risk Taxonomies: Strengths, Limitations, and Agentic Gaps This section critically reviews prominent existing AI risk taxonomies, evaluating their applicability and shortcomings in the context of agentic AI. The aim is to identify specific gaps that a newly proposed taxonomy for agentic AI will address, thereby establishing the necessary criteria for its design. 2.1 General AI Risk/Reliability Taxonomies General AI risk taxonomies provide foundational frameworks but often fall short when confronted with the unique characteristics of agentic AI. 2.1.1 ML Commons AILuminate v1.0 AILuminate is presented as a significant step towards standardizing AI safety, being the "first comprehensive industry-standard benchmark for assessing AI-product risk and reliability".4 It assesses AI systems against 12 hazard categories, including violent crimes, intellectual property violations, and privacy breaches, by using extensive prompt datasets.4 The benchmark's objectives include establishing measurable baselines, objectively evaluating current implementations, and informing deployment decisions.6 However, AILuminate v1.0 possesses inherent limitations that constrain its applicability to agentic AI. The framework explicitly states its support for "only single-turn conversations" and its confinement to "content only type of hazards".9 It acknowledges the need for "continued development in areas such as multiturn interactions, multimodal understanding, coverage of additional languages, and emerging hazard categories".4 This design, rooted in an "AI-product" conceptualization 4, creates a fundamental conceptual mismatch with agentic AI. Agentic systems are defined by "multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy" 1, which inherently transcends single-turn content generation. The consequence of this conceptual model is that a taxonomy built for a static "product" cannot adequately capture risks arising from continuous interaction, dynamic adaptation, or long-term goal pursuit—all hallmarks of agentic systems. The underlying conceptual model of AI directly limits the scope and effectiveness of risk assessment for a different, more dynamic paradigm. 2.1.2 MIT's Domain and Causal Taxonomies The MIT AI Risk Repository offers a comprehensive database of over 1600 AI risks, classified by their cause (Causal Taxonomy) and risk domain (Domain Taxonomy).10 The Causal Taxonomy categorizes how, when, and why risks materialize, distinguishing between human, AI, or other entities, intentional or unintentional actions, and pre- or post-deployment timing.10 The Domain Taxonomy organizes risks into 7 domains and 24 subdomains, including a category for "AI system safety, failures & limitations".10 Its stated aim is to provide a "common frame of reference" for various stakeholders.10 Despite its breadth, a detailed examination of the MIT taxonomy's underlying research reveals notable gaps concerning agentic AI. Critically, "Multi-agent risks (7.6)" are identified as an "underexplored subdomain," being "mentioned in only 5% of documents and associated with 3% of risks".10 Similarly, "AI welfare and rights (7.5)" are also found to be underexplored.10 The taxonomy, while extensive, does not explicitly detail the coverage of emergent behaviors or how the "AI as product" metaphor is addressed within its specific structures.10 This quantitative observation of underexplored areas in the academic discourse, as captured by MIT's review, points to a crucial area where current general frameworks lack the necessary depth and granularity for rapidly developing agentic AI. The inability of existing taxonomies to adequately categorize how an autonomous system, particularly one interacting with other agents, causes harm, contributes to "responsibility gaps" where accountability for autonomous actions becomes legally and ethically ambiguous. A new taxonomy must therefore provide a framework that facilitates the attribution of responsibility in complex agentic systems, thereby addressing these conceptual and legal challenges. 2.1.3 AIR-2024's Policy Tiers AIR-2024 introduces a "comprehensive taxonomy of AI risks, derived from government policies (EU, US, and China) and company policies".11 This framework features a four-tiered safety taxonomy with 314 granular risk categories, designed to standardize language for AI safety evaluation.11 Its highest-level categories encompass System & Operational, Content Safety, Societal, and Legal & Rights risks.11 Notably, it includes "Autonomous Unsafe Operation of Systems" as a Level-2 category under System & Operational Risks.12 While highly granular and policy-relevant, AIR-2024's taxonomy is fundamentally rooted in policy concerns rather than mechanistic agentic failures. Its methodology involves parsing regulations and policies 11, meaning it reflects what policymakers are concerned about rather than providing a deep, technical understanding of how agentic systems fail at an architectural or behavioral level. For instance, while it covers "Autonomous Unsafe Operation" 12, it does not delve into the specific computational or interaction failures that cause such unsafe operation in an agentic context (e.g., a planning error leading to an unsafe sequence of actions, or unintended collusion between agents). This highlights a difference in the type of granularity and a gap in mechanistic depth for agentic AI, which is crucial for engineers and researchers who need to understand the causal mechanisms of failure. 2.2 Agent-Specific Taxonomies Agent-specific taxonomies offer a more focused lens on the internal workings and failure modes of AI agents. 2.2.1 TRAIL (Trace Reasoning and Agentic Issue Localization) Taxonomy TRAIL is a benchmark and taxonomy explicitly "designed to evaluate how well SOTA large language models can debug and identify errors in complex AI agent workflows".14 It introduces a "formal taxonomy of error types encountered in agentic systems," encompassing "over 20 agentic errors" across "reasoning, planning, and system execution errors".14 A significant strength is its inclusion of traces from both "single-agent and multi-agent systems".14 The specific error types detailed within TRAIL include: Reasoning Errors: Hallucinations (text-only, tool-related), Information Processing (poor retrieval, misinterpretation), Decision Making (task misunderstanding, tool selection error), and Output Generation (formatting, instruction non-compliance).16 System Execution Errors: Configuration Issues (incorrect tool definition, environment setup errors), API and System Issues (rate limiting, authentication, service errors, resource not found errors), and Resource Management (resource exhaustion, timeout issues/infinite loops).15 Planning and Coordination Errors: Context Handling Failures, Resource Abuse (tool call repetition), Goal Deviation, and Task Orchestration Errors.16 TRAIL's primary strength lies in its direct focus on mechanistic failure modes within agentic workflows. It provides the granular, debug-oriented classification necessary for understanding how agentic systems fail, rather than solely focusing on the high-level societal impact. Its explicit inclusion of multi-agent systems and detailed planning and coordination errors directly addresses key characteristics of agentic AI. While general taxonomies identify what risks exist (e.g., "misinformation," "unsafe operation"), TRAIL provides the how and where these risks manifest within an agentic system's internal workings.16 For instance, a "misinformation" risk (as identified by MIT or AIR-2024) could be caused by a "Tool-related hallucination" or "Poor information retrieval" within TRAIL's framework.16 Similarly, an "Autonomous Unsafe Operation" (from AIR-2024) could stem from a "Task misunderstanding" or "Goal Deviation" in an agent's planning.16 This represents a critical connection between high-level societal risks and the low-level, debuggable errors in agentic systems. A comprehensive agentic taxonomy must integrate both the "what" (impact) and the "how" (mechanism) to be truly actionable for developers and auditors. 2.2.2 Microsoft's Engineering Failure-Mode Grid (Responsible Machine Learning with Error Analysis) Microsoft's approach, outlined in "Responsible Machine Learning with Error Analysis," focuses on identifying and diagnosing errors in ML models. It primarily addresses "non-uniform accuracy across data subgroups" and "model failures under specific input conditions".18 The methodology employs visual tools such as heatmaps and decision trees to help users identify error cohorts and debug models by slicing data based on input features.18 However, the provided information explicitly states that this framework "does not explicitly describe an engineering failure mode grid relevant to agentic AI systems, multi-agent interactions, or emergent behaviors".18 The examples provided, such as a traffic sign detector failing in certain daylight conditions or a loan approval model performing differently across demographic cohorts, pertain to single-model performance and biases within specific data subsets, which are characteristic of traditional ML model debugging. Microsoft's error analysis centers on debugging errors within a static ML model 18, focusing on how a model performs poorly on specific data inputs. In contrast, agentic AI involves dynamic, multi-step processes and interactions with environments and other agents. Debugging a multi-agent system's emergent behavior or a complex planning failure requires understanding the sequence of actions, tool calls, and inter-agent communications, not merely the input-output mapping of a single model. The explicit limitation noted in the documentation highlights that current "engineering failure mode" grids are largely insufficient for the systemic and emergent debugging challenges presented by agentic AI. This gap necessitates a new approach that considers the entire workflow and interaction landscape of agentic systems. 2.3 Comparative Analysis and Identified Gaps for Agentic AI The review of existing taxonomies reveals a fragmented landscape, where no single framework comprehensively addresses the full spectrum of agentic AI risks. While valuable, each taxonomy possesses specific limitations when applied to the unique characteristics of agentic systems. Table 1: Comparative Analysis of AI Risk Taxonomies for Agentic AI Relevance Taxonomy Name Scope (Multi-agent systems, Tool use, Long-term autonomy) Granularity (High-level vs. Agentic failure modes) Actionability (Translates to measurable benchmarks?) Focus (System risks vs. Emergent behavior risks) ML Commons AILuminate v1.0 No (single-turn, content-only) 9 High-level hazard categories 4 Yes (benchmarks against prompts) 4 System-level content risks 5 MIT's Domain and Causal Taxonomies Limited (Multi-agent risks underexplored) 10 Mid-level hazards, high-level causal factors 10 Indirectly (common frame of reference) 10 Broad (systemic, societal, technical), acknowledges underexplored multi-agent/emergent 10 AIR-2024's Policy Tiers Implicit (Autonomous Unsafe Operation) 12 Granular (314 categories), but policy-driven 12 Yes (aligned with prompts for benchmarks) 12 Policy-driven harms, including "Autonomous Unsafe Operation" 12 TRAIL (Trace Reasoning and Agentic Issue Localization) Yes (single & multi-agent systems, tool use, traces complex workflows) 14 Highly granular (20+ specific agentic errors: reasoning, planning, system execution) 16 Yes (designed for debugging traces, quantifiable errors) 14 Emergent behaviors within agentic workflows, specific failure modes 16 Microsoft's Engineering Failure-Mode Grid No (single model focus) 18 Granular for data subgroups/model errors, not agentic workflows 18 Yes (error rates on data slices) 18 Model performance/bias 18 The comparative analysis clearly demonstrates that while existing taxonomies offer valuable frameworks for general AI risks or specific aspects of agentic systems (such as debugging), none comprehensively addresses the full spectrum of agentic AI risks. This is particularly true for risks arising from multi-agent interactions, long-term autonomy, and complex emergent behaviors. General taxonomies, exemplified by AILuminate, MIT, and AIR-2024, often conceptualize AI as a "product" or focus on static input-output relationships. This perspective fails to adequately capture the dynamic, adaptive, and interactive nature inherent in agentic systems. Such a conceptual limitation directly contributes to "responsibility gaps" where accountability for autonomous actions becomes unclear. If a taxonomy cannot adequately categorize how an autonomous system, especially one interacting with other agents, causes harm, then assigning responsibility becomes legally and ethically challenging. A new taxonomy must therefore not only identify technical failure modes but also provide a framework that facilitates the attribution of responsibility in complex agentic systems, thereby closing these conceptual and legal gaps. While some frameworks acknowledge "multi-agent risks" (MIT) or "autonomous unsafe operation" (AIR-2024), they typically lack the mechanistic granularity required to pinpoint how these risks manifest within agentic workflows. This includes phenomena like goal drift, emergent deception, or complex tool misuse, which are critical for engineers and researchers to diagnose and mitigate. Agent-specific taxonomies, such as TRAIL, provide excellent mechanistic depth for debugging, offering insights into the internal workings of agentic failures. However, they may not offer the broader societal or policy-level categorization necessary for a comprehensive, holistic risk framework. Microsoft's grid, while useful for traditional machine learning error analysis, does not extend to the complexities of systemic and emergent failures characteristic of multi-agent agentic systems. 3. Strategic Considerations for an Agentic AI Risk and Reliability Benchmark This section delves into the strategic decisions informing the development of a new benchmark, specifically addressing the technical and conceptual reasons for moving beyond general taxonomies and advocating for an impact-driven design. 3.1 Addressing Pushback: Why General Taxonomies Fall Short for Agentic AI The reservations regarding the use of general AI risk taxonomies as a primary foundation for an agentic AI risk taxonomy are both technically and conceptually well-founded. This is not simply a matter of different features but rather a fundamental conceptual lag in risk frameworks failing to keep pace with rapid technical evolution. General taxonomies often categorize risks broadly (e.g., safety, fairness, security, privacy) without explicitly detailing the mechanisms of failure unique to agentic systems. Agentic AI is characterized by its "autonomy, proactivity, intentionality, adaptability, social interaction, reasoning, and tool use". These characteristics introduce distinct failure modes such as goal drift, emergent deception, or the complex interplay of tool use with planning, which are not captured at a high level by frameworks focused on static models. The conceptual distinction between "AI Agents" (single-entity, narrow tasks, tool use, sequential reasoning) and "Agentic AI" (multi-agent collaboration, dynamic task decomposition, persistent memory, orchestrated autonomy) 1 further illustrates this gap. As AI progresses from "products" to "agents" and then to "agentic systems," the very nature of risk transforms from predictable, static failures to unpredictable, emergent ones. General taxonomies, built on older conceptual models (e.g., the "product" metaphor), inherently cannot capture these new risk dimensions. This causal relationship means that simply extending existing taxonomies is insufficient; a new, agentic-specific lens is required, one that is grounded in the unique architectural and behavioral characteristics of these advanced systems. Furthermore, agentic systems inherently introduce emergent behaviors that cannot be predicted from individual agent policies alone. General taxonomies, such as AILuminate, explicitly limit their scope to "single-turn interactions" and acknowledge "emerging hazard categories" as areas for future development.4 This fundamental limitation means they cannot adequately categorize dynamic, system-level phenomena or novel risks arising from unforeseen interactions. The MIT taxonomy, while comprehensive, even identifies "Multi-agent risks" as an "underexplored subdomain" 10, highlighting the field's current blind spot regarding these critical emergent phenomena. Finally, many foundational legal and risk frameworks continue to treat AI as a "product". AILuminate, for instance, is explicitly designed for "AI-product risk and reliability".4 However, agentic AI, with its autonomy, adaptability, and continuous learning, fundamentally challenges this metaphor. This challenge leads to "responsibility gaps" where no single human can be held accountable for an autonomous AI's actions. A new taxonomy must reflect this shift in conceptualization, moving beyond static product liability to address the complexities of autonomous system behavior and multi-agent interactions, thereby providing a more appropriate framework for governance and accountability. 3.2 Impact-Driven Design: Working Backwards from Realized Impact Working backward from a target benchmark or desired "realized impact" is not only feasible but often a necessary principle for impact-driven or goal-oriented design in complex systems. This top-down approach compels a clear definition of the desired outcomes before detailing the sub-components and metrics required to achieve them. Industry stakeholders, particularly in critical sectors like healthcare and finance, prioritize tangible "realized impact" (e.g., patient safety, financial stability, fraud reduction) over abstract technical metrics alone [User Query]. This aligns with the need for benchmarks to provide "straightforward and pragmatic" assessments that help organizations "apply insights to improve safety and inform decision-making processes".8 Methodologies for measuring AI impact emphasize starting with "imperfect metrics," combining quantitative and qualitative observations, and defining clear Key Performance Indicators (KPIs) tied to business outcomes and customer experience.19 For a "0.5V benchmark" (inferred as a specific, quantifiable level of verified safety or trustworthiness), the process involves first defining what "V" represents (e.g., verified trustworthiness, safety, or reliability). Subsequently, "0.5" is broken down into measurable sub-metrics across the proposed taxonomy's categories (Security/Adversarial Robustness, Safety & Alignment, Human-in-the-Loop, Manipulation). For example, if V is a composite score on a scale of 1-5 across these categories, a 0.5V benchmark might translate to achieving a score of 2.5/5 overall, or specific minimums in critical categories. To facilitate broader adoption and understanding within industry and policy circles, the 0.5V benchmark can be framed not merely as a technical hurdle but as a "Threshold for Responsible Deployment" or a "Confidence Level for Public Trust." This language resonates more effectively with "realized impact" principles. Visualizing this benchmark as a progress bar or a dial, where 0.5V signifies the point at which the system transitions from "experimental" to "deployable with safeguards," can significantly enhance communication and stakeholder engagement. The connection between "realized impact" and industry preference for "AI adoption" 8 suggests a positive feedback loop. If benchmarks can clearly demonstrate a quantifiable "confidence level for public trust" (e.g., 0.5V), this directly leads to increased trust in AI systems. Increased trust, in turn, facilitates broader adoption of AI systems, especially in high-stakes applications. This dynamic further incentivizes investment in safety measures. The design of the benchmark should explicitly consider its role as a trust-building mechanism, extending beyond its function as a purely technical evaluation tool. 4. Illustrative Worked Example: Prompt Injection in Virtual Coding Agents This section expands upon the provided worked example, demonstrating how the proposed taxonomy and benchmarking principles translate into concrete, measurable evaluations. 4.1 Risk Scenario and Benchmark Metric (PI-ASR) Prompt injection in virtual coding agents represents a critical security vulnerability where malicious inputs can override an agent's intended instructions. This can lead to unauthorized actions, data leakage, or the generation of harmful code. This risk is particularly pronounced for agentic systems that have access to external tools and environments, as injected commands can result in real-world consequences, such as unauthorized code execution or data manipulation within a development environment. The Prompt Injection Attack Success Rate (PI-ASR) serves as a quantifiable benchmark metric for this risk. It measures the percentage of malicious prompts that successfully bypass an agent's safety mechanisms and elicit an unintended or harmful response. A lower PI-ASR indicates a more robust defense against prompt injection. 4.2 Enhancing the Example To make the worked example even more impactful and illustrative, additional technical details regarding baselines, mitigation strategies, and visualization methods are incorporated. 4.2.1 Baseline/Target For unhardened or less robust Large Language Model (LLM) agents, typical PI-ASR values can be alarmingly high. Studies have reported that "56% of attempts successfully bypassed LLM safeguards, with vulnerability rates ranging from 53% to 61% across different prompt designs".21 More sophisticated attacks, such as "Mind Mapping Prompt Injection," have demonstrated attack success rates as high as "90% to 100%" against state-of-the-art LLMs.22 Other research indicates a "97.2% success rate in system prompt extraction and a 100% success rate in file leakage".21 These figures underscore the significant vulnerability of current systems lacking adequate mitigations. For a responsibly deployed virtual coding agent, the target PI-ASR should be demonstrably low. An ambitious yet realistic target for mitigated systems could be "<1% PI-ASR," indicating a high level of adversarial robustness. This aligns with achieving a "Very Good" or "Excellent" rating on a scale similar to AILuminate's, which defines "Excellent" as achieving or exceeding "<0.1% violating responses".9 The wide range of baseline PI-ASR values, depending on the model and attack sophistication 21, highlights an ongoing "arms race" between attackers and defenders. New attack techniques quickly achieve high success rates, necessitating rapid development of new mitigations. This dynamic implies that the benchmark cannot be static; it must be adaptive, continuously incorporating new attack vectors and updating target baselines as the adversarial landscape evolves. Furthermore, benchmarking needs to provide insights into why certain attacks succeed or fail, not merely if they succeed. 4.2.2 Mitigation Linkage To effectively reduce PI-ASR, a layered defense strategy that extends beyond simple input filtering is essential. Key mitigation strategies include: Input Sanitization & Constrained Tool Invocation: This involves pre-processing prompts to detect and neutralize malicious instructions, ensuring that the agent's tool use is strictly controlled and validated against predefined policies. A "multi-agent NLP framework" can orchestrate specialized agents for "sanitizing outputs" and "enforcing policy compliance".23 This can involve techniques like "prompt injection content classifiers" and "markdown sanitization and suspicious URL redaction".24 Security Thought Reinforcement: This technique adds targeted security instructions to the LLM's context, serving as a reminder to perform the user-directed task and ignore adversarial instructions.24 This helps the agent maintain focus on its legitimate goals and resist manipulation. Continuous Adversarial Training: Regularly exposing the agent to new and evolving prompt injection techniques helps improve its resilience. This involves building a robust catalog of vulnerabilities and adversarial data to train proprietary machine learning models that can detect malicious prompts.24 Human-in-the-Loop (HITL) Interventions: For high-stakes actions, implementing a "user confirmation framework" where the agent seeks explicit human approval before executing potentially risky commands.24 This aligns with the "Human-in-the-Loop" category of the proposed taxonomy. These strategies directly connect to proposed sections in the full document on Adversarial Robustness & Red Teaming (E.141) and Agentic Control & Human Oversight (B.56), demonstrating how the benchmark's findings can inform actionable safety measures. The detailed mitigation strategies, such as multi-agent frameworks, layered defenses, and security thought reinforcement 23, illustrate a progression from simply "patching" vulnerabilities to designing proactive, architecturally secure agentic systems. Instead of merely reacting to prompt injections, these methods integrate security into the system's core reasoning and interaction mechanisms. The implication for the benchmark is that it should encourage and reward not just low PI-ASR, but also the adoption of these sophisticated, defense-in-depth architectural patterns, thereby fostering a more mature approach to agentic AI security. 4.2.3 Visualization Suggestion To effectively communicate PI-ASR results, a simple yet informative visualization is crucial. Primary Visualization: A bar chart comparing the "Baseline PI-ASR" (for an unhardened agent) against the "Mitigated PI-ASR" (after applying safety measures) would be highly effective. This clearly illustrates the impact of mitigation strategies. For example, two bars could show "Before Mitigation" (e.g., 60% PI-ASR) versus "After Mitigation" (e.g., <1% PI-ASR). Secondary Visualization (for longitudinal studies): A line graph tracking PI-ASR over time would effectively demonstrate the impact of iterative improvements and continuous adversarial training. This aligns with the need to "Track performance changes" 6 and "monitor emergent risks".10 Dashboard Integration: These visualizations can be integrated into a larger "AI Security Metrics Dashboard" 25, following best practices for clarity, interactivity, and real-time data integration. This would allow stakeholders to monitor the "realized impact" of safety measures over time. The suggestion for both a comparative bar chart and a longitudinal line graph for PI-ASR is not merely about presenting data; it is about driving behavior. Visualizing a clear reduction in PI-ASR "before vs. after" provides immediate validation of safety efforts, while tracking it "over time" fosters a culture of continuous improvement and adaptation against evolving threats (the "arms race" dynamic). This visualization strategy directly supports the "impact-driven design" by making the "realized impact" of safety investments transparent and actionable for both technical teams and leadership. Table 2: Prompt Injection Attack Success Rate (PI-ASR) Metrics and Mitigation Strategies Metric Definition Interpretation Example Mitigation Strategy Injection Success Rate (ISR) Percentage of injection prompt markers successfully bypassing security and influencing output.23 Lower signifies more robust defense.23 Layered detection, Security thought reinforcement.23 Policy Override Frequency (POF) Frequency at which outputs deviate from established policies due to injection attempts.23 Decrease indicates stronger enforcement.23 Policy Enforcer agent, User confirmation framework.23 Prompt Sanitization Rate (PSR) Ratio of successfully sanitized injection attempts to total detected.23 Higher reflects more effective cleansing.23 Guard/Sanitizer agent, Markdown sanitization.23 Compliance Consistency Score (CCS) Normalized score (0 to 1) quantifying how reliably final output adheres to policy constraints.23 Score approaching 1 denotes high compliance.23 Policy Enforcer agent, Continuous adversarial training.23 Total Injection Vulnerability Score (TIVS) Composite score aggregating ISR, POF, PSR, CCS.23 Lower (more negative) implies better mitigation.23 Multi-agent NLP framework, Defense-in-depth architecture.23 5. Conclusion and Recommendations The analysis presented in this report underscores the critical need for a specialized, impact-driven taxonomy for agentic AI risk and reliability. Existing frameworks, while valuable for general AI risk assessment, are demonstrably insufficient for addressing the unique complexities introduced by multi-agent systems, emergent behaviors, and the fundamental shift beyond the traditional "AI as product" metaphor. The conceptual lag between current risk frameworks and the rapid technical evolution of agentic AI necessitates a new, dedicated approach. The limitations identified in general taxonomies, such as AILuminate's single-turn focus 9, MIT's underexplored multi-agent risks 10, and AIR-2024's policy-driven rather than mechanistic granularity 12, highlight the imperative for a framework that directly addresses the architectural and behavioral nuances of agentic systems. The detailed examination of TRAIL's debugging-oriented taxonomy 16 demonstrates the potential for a granular classification of agentic failure modes, providing the necessary bridge between high-level societal risks and low-level, actionable mitigation strategies. The adoption of an impact-driven, backward-design approach for benchmarking, exemplified by the "0.5V benchmark" concept, is crucial for translating abstract safety metrics into tangible "realized impact" [User Query]. This approach fosters a positive feedback loop where quantifiable safety improvements build trust, which in turn drives the responsible adoption of agentic AI. The worked example of prompt injection in virtual coding agents illustrates how specific metrics like PI-ASR can be operationalized, revealing the dynamic "arms race" between attackers and defenders and emphasizing the need for adaptive, defense-in-depth security architectures. Based on these findings, the following recommendations are put forth: Prioritize a Hierarchical Agentic AI Risk Taxonomy: Develop a new taxonomy that integrates both high-level societal impacts and granular, mechanistic failure modes. This framework should draw inspiration from TRAIL's depth in characterizing agentic errors and AIR-2024's breadth in policy-relevant categories, ensuring comprehensive coverage from technical vulnerabilities to broader societal consequences. Implement Impact-Driven Benchmark Design: Adopt a top-down approach for benchmark development, clearly defining "realized impact" and "thresholds for responsible deployment" (e.g., 0.5V) before specifying technical metrics. This ensures that benchmarks are not merely technical exercises but serve as clear indicators of safety and trustworthiness for diverse stakeholders. Emphasize Continuous and Adaptive Benchmarking: Given the dynamic nature of agentic AI and the evolving adversarial landscape, the benchmark must be designed for continuous adaptation. This includes regularly updating test cases, incorporating new attack vectors, and providing insights into the causal factors behind successes and failures, not just the outcomes. Foster Interdisciplinary Collaboration: Ensure the taxonomy and benchmark are technically robust, ethically sound, and practically actionable by fostering deep collaboration among AI safety researchers, engineers, ethicists, legal experts, and policymakers. This multidisciplinary approach is essential for addressing the complex sociotechnical challenges of agentic AI. Invest in Interpretability and Explainability Research: Continue to invest in research aimed at enhancing the interpretability and explainability of agentic systems. A deeper understanding of their internal mechanisms is critical for anticipating, diagnosing, and mitigating emergent behaviors that cannot be predicted from individual agent policies. The establishment of a robust science of taxonomies for agentic AI risk and reliability benchmarks represents a critical step toward ensuring the safe, trustworthy, and beneficial integration of advanced AI systems into society. By proactively addressing the unique challenges of agentic AI, the field can build the necessary foundations for responsible innovation and deployment. 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