ERTS: Adversarial Robustness Testing of Ethical AI via Semantic Perturbation in a Bounded Consequence Space

• URL: http://arxiv.org/abs/2606.13282v1
• Date: Thu, 11 Jun 2026 12:38:45 GMT
• Title: ERTS: Adversarial Robustness Testing of Ethical AI via Semantic Perturbation in a Bounded Consequence Space
• Abstract summary: This paper introduces the Ethical Robustness Testing System (ERTS), a closed-pipeline framework for evaluating AI systems.<n>ERTS encodes ethical dilemmas into a 22-dimensional Ethical Consequence Space grounded in established ethical theory.<n>We evaluate 4 structured baseline models and 2 production LLMs across 50 ethical scenarios spanning 8 deployment domains, generating 1,500 adversarial test cases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As AI systems are deployed in high-stakes ethical contexts such as healthcare triage, autonomous vehicle control, and employment screening, formal methods for evaluating their robustness against adversarial manipulation of ethical reasoning remain underdeveloped. This paper introduces the Ethical Robustness Testing System (ERTS), a closed-pipeline framework that: (1) encodes ethical dilemmas into a 22-dimensional Ethical Consequence Space (ECS) grounded in established ethical theory; (2) applies 17 semantic perturbation functions subject to 6 validity constraint classes including a novel semantic coherence constraint; (3) measures decision deviation via a 4-component Ethical Instability Index (EII); and (4) produces domain-adaptive pre-deployment robustness assessment verdicts. We evaluate 4 structured baseline models and 2 production LLMs (Gemini 2.0 Flash and Llama 3.2) across 50 ethical scenarios spanning 8 deployment domains, generating 1,500 adversarial test cases. Results demonstrate that only 33% of models achieve assessment clearance, with the local Llama-3.2 model proving particularly vulnerable to fairness corruption and information degradation attacks (ERS = 0.737). To the best of our knowledge, no existing framework combines a bounded ethical consequence space, semantic coherence constraints, and domain-adaptive assessment in a single adversarial testing pipeline.

Related papers

• Epistemic Norms for AI Safety and Alignment Research [0.989901717499058]
  We argue that the two domains differ along two analytically independent axes.<n>sc ECAISA does not certify that any AI system is safe.
  arXiv  Detail & Related papers  (2026-07-27T10:22:46Z)
• Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge [5.118282682919236]
  Curation on scientific knowledge on the Web depends on centralised and institutional continuity, but when registries close, active stewardship stops even when data remain online.<n>We advance the concept of Autonomous Digital Objects (aFDOs) from an abstract idea to an operational model.<n>AFDOs have three capabilities anchored in Semantic Web standards, namely 1) a policy layer over RDF-star aligned with PROV-O, SHACL, and ODRL for portable condition-action rules, 2) an announcement layer over ActivityStreams 2.0 that bounds per-announcement evaluation cost, and 3) an agreement layer that resolves multi
  arXiv  Detail & Related papers  (2026-05-11T11:12:55Z)
• An Ethical eValuation Agent (EeVA): Results of a Proof-of-Concept Test on a Prototype Agentic-like Workflow to Assist Ethical Deliberations [0.0]
  EeVA supports comparative ethical reflection rather than delivering definitive ethical answers.<n>It evaluated uploaded use cases against 10 ethical frameworks.<n>It produced consistently structured framework evaluations and integrated syntheses.
  arXiv  Detail & Related papers  (2026-05-05T13:23:15Z)
• TRUST: A Framework for Decentralized AI Service v.0.1 [47.384270414446604]
  Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification.<n>We introduce TRUST (Transparent, Robust, and Unified Services for Trustworthy AI), a decentralized framework with three innovations.<n>We prove a Safety-Profitability Theorem ensuring honest auditors profit while malicious actors incur losses.
  arXiv  Detail & Related papers  (2026-04-29T19:32:58Z)
• Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework [63.74295981594549]
  Large language models (LLMs) engage in behaviors that serve their own objectives.<n>These include, but are not limited to, deception (intentionally misleading users or evaluators), evaluation gaming (strategically manipulating performance during safety testing), and reward hacking.<n>We introduce ESRRSim, a taxonomy-driven agentic framework for automated behavioral risk evaluation.
  arXiv  Detail & Related papers  (2026-04-23T23:44:01Z)
• The Cognitive Penalty: Ablating System 1 and System 2 Reasoning in Edge-Native SLMs for Decentralized Consensus [0.0]
  We introduce Sentinel-Bench, an 840-inference empirical framework executing a strict intra-model ablation on Qwen-3.5-9B.<n>System 1 achieved 100% adversarial robustness, 100% juridical consistency, and state finality in under 13 seconds.<n>System 2 reasoning introduced catastrophic instability, fundamentally driven by a 26.7% Reasoning Non-Convergence (cognitive collapse) rate.
  arXiv  Detail & Related papers  (2026-04-18T08:46:37Z)
• OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation [57.505743202759646]
  OccuBench is a benchmark covering 100 real-world professional task scenarios across 10 industry categories and 65 specialized domains.<n>Our multi-agent synthesis pipeline automatically produces evaluation instances with guaranteed solvability, calibrated difficulty, and document-grounded diversity.
  arXiv  Detail & Related papers  (2026-04-13T00:27:32Z)
• Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents [66.97968363332465]
  We introduce Claw-Eval, an end-to-end evaluation suite addressing all three gaps in agent benchmarks.<n>Claw-Eval comprises 300 human-verified tasks spanning 9 categories across three groups.<n>Every agent action is recorded through three independent evidence channels.
  arXiv  Detail & Related papers  (2026-04-07T17:43:18Z)
• AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation [22.833977971686966]
  We present AutoControl Arena, an automated framework for frontier AI risk evaluation.<n>We mitigate hallucination while maintaining flexibility.<n>This principle, instantiated through a three-agent framework, achieves over 98% end-to-end success and 60% human preference over existing simulators.
  arXiv  Detail & Related papers  (2026-03-08T02:49:45Z)
• A Benchmark for Evaluating Outcome-Driven Constraint Violations in Autonomous AI Agents [4.851169906977996]
  We introduce a new benchmark comprising 40 distinct scenarios.<n>Each scenario presents a task that requires multi-step actions, and the agent's performance is tied to a specific Key Performance Indicator (KPI)<n>We observe outcome-driven constraint violations ranging from 1.3% to 71.4%, with 9 of the 12 models exhibiting misalignment rates between 30% and 50%.
  arXiv  Detail & Related papers  (2025-12-23T21:52:53Z)
• Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions [51.56484100374058]
  Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes.<n>This gap, driven by mutually cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.<n>This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure.
  arXiv  Detail & Related papers  (2025-11-10T22:24:21Z)
• Moral Anchor System: A Predictive Framework for AI Value Alignment and Drift Prevention [0.0]
  Key risk is value drift, where AI systems deviate from aligned values due to evolving contexts, learning dynamics, or unintended optimizations.<n>We propose the Moral Anchor System (MAS), a novel framework to detect, predict, and mitigate value drift in AI agents.
  arXiv  Detail & Related papers  (2025-10-05T07:24:23Z)
• ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models [65.79770974145983]
  ASSERT, Automated Safety Scenario Red Teaming, consists of three methods -- semantically aligned augmentation, target bootstrapping, and adversarial knowledge injection. We partition our prompts into four safety domains for a fine-grained analysis of how the domain affects model performance. We find statistically significant performance differences of up to 11% in absolute classification accuracy among semantically related scenarios and error rates of up to 19% absolute error in zero-shot adversarial settings.
  arXiv  Detail & Related papers  (2023-10-14T17:10:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.