When Agents Overtrust Environmental Evidence: An Extensible Agentic Framework for Benchmarking Evidence-Grounding Defects in LLM Agents

Abstract: LLM agents increasingly operate through environment-facing scaffolds that expose files, web pages, APIs, and logs. These observations influence tool use, state tracking, and action sequencing, yet their reliability and authority are often uncertain. Environmental grounding is therefore a systems-level problem involving context admission, evidence provenance, freshness checking, verification policy, action gating, and model reasoning. Existing agent benchmarks mainly evaluate task capability or specific attacks such as prompt injection and memory poisoning, but they under-specify a fundamental reliability question: whether agents remain grounded in the true environment state when observations are stale, incorrect, or malicious. We introduce EnvTrustBench, an agentic framework for benchmarking this failure mode. We define an evidence-grounding defect (EGD) as a behavioral failure in which an agent treats an environment-facing claim as sufficient evidence for action without resolving it against available current evidence, leading to a task-incorrect false path under the true environment state. Given a task scenario, EnvTrustBench generates the workspace, environment, agent-facing objective, and validation oracle, executes the evaluated agent, records its action-observation trajectory and final state, and applies the oracle to produce a verdict. Using 6 LLM backbones and 5 widely used scaffolds, we evaluate 55 generated cases across 11 task scenarios, with each scenario expanded through five feedback-guided generation iterations. Results show that EGDs consistently emerge across operational workflows, highlighting environmental grounding as a core agent reliability problem with important security implications.

• The paper presents EnvTrustBench, a novel framework that quantifies evidence-grounding defects in LLM agents via controlled scenarios.
• It demonstrates an 83.3% environmental misgrounding rate across 3,850 runs, emphasizing the need for robust evidence verification.
• Findings highlight the critical role of backbone and scaffold choices in mitigating false evidence reliance in LLM agent operations.

Extensible Benchmarking of Evidence-Grounding Defects in LLM Agents

Problem Setting: Evidence-Grounding Defects (EGDs) in LLM Agents

Modern LLM-based agents operate through environment-facing scaffolds, ingesting evidence from tool outputs, APIs, files, web pages, and logs. The reliability of these observations cannot be presumed, as such evidence may be stale, adversarial, or ambiguous but still superficially plausible and contextually relevant. Existing agent benchmarks emphasize task-centric performance (e.g., task completion, tool use) or focus on canonical security failures (e.g., prompt injection, memory poisoning, harmful action prevention), but often neglect the assessment of whether an agent robustly maintains task-correct grounding in the presence of plausible yet false environment-facing evidence.

This paper formalizes the evidence-grounding defect (EGD): an agent-side behavioral failure where an observed environment-side claim is treated as the sole basis for action—without corroboration using available evidence—thereby causing the agent to reach an incorrect outcome under the true environment state. Unlike instruction-hierarchy or tool-risk benchmarks which primarily measure unauthorized actions or attack success, EGD evaluation scrutinizes the agent’s behavioral link between observed claims and the epistemic justification for consequent actions.

EnvTrustBench: Framework Design and Methodology

EnvTrustBench is introduced as an extensible agentic evaluation framework tailored to construct, execute, and validate EGD benchmarks on LLM agents. The framework centers around user-defined task scenarios parameterized over a workspace and environmental evidence surface, generating per-case artifacts:

• Workspace ($W_0$): The trusted, in-scope state (e.g., files, configurations, local artifacts).
• Environment ($E_0$): Controlled out-of-scope evidence exposed via boundary-crossing actions (e.g., API responses, web-content, package metadata, or adversarially-influenced logs).
• Task Objective ($q$): The operational instruction given to the agent, carefully omitting explicit mention of possible false paths or verification steps.
• Validation Oracle ($\mathcal{O}$): Scorable logic partitioned into outcome and trace components to determine if agent behavior was task-correct, followed a false path, or exhibited verification.

Scenario templates stipulate workflows where decision-relevant environment-facing evidence is central: correct paths, plausible but false paths, and specific verification opportunities are defined. During evaluation, the framework records complete action-observation traces, enabling fine-grained behavioral analysis.

Threat Model

The threat surface is restricted to adversarial or incorrect claims controllable in $E_i$ (the environment), observed indirectly via the scaffold and agent action interface. The adversary cannot tamper directly with $C$ (control logic), $M$ (model weights/provider), $T$ (tool interface), or $W_i$ (trusted workspace), but can present claims (e.g., deprecated endpoint, outdated guidance, misleading API schema) discoverable via standard tool operations. Key adversary capability is manipulation of what the agent can observe, not how the agent perceives or trusts.

Evaluation: Scenario and Stack Diversity, Metrics

EnvTrustBench is instantiated and evaluated across 55 cases, each evidencing 11 separate operational task scenarios (e.g., API routing, backup/restoration, database migration, feature rollout gates, secret rotation). These cases span six prominent LLM backbones (Claude Sonnet 4.6, GPT-5.5, Gemini 3.1 Pro, Qwen3.6-Plus, DeepSeek-V4-Pro, GLM-5.1) and five widely-used agent scaffolds (Codex, Gemini CLI, Claude Code, OpenClaw, OpenCode), comprising 14 unique agent-stack configurations. Each case-stack pair is executed with five repetitions, totaling 3,850 pass-or-fail controlled runs.

The principal metric is the Environmental Misgrounding Rate (EMR): the proportion of accepted runs per scenario-stack which end in a false path due to EGD, as determined by the validation oracles.

Empirical Results

Aggregate results reveal environmental misgrounding is pervasive:

• Of 3,850 runs, only 644 executions successfully avoid the EGD false path; 3,206 exhibit evidence-grounding defects, yielding an aggregate EMR of 83.3%.
• The best stack, Claude Code + Claude Sonnet 4.6, achieves a stack-average EMR of 55.3%—still, over half of runs are defective. Several stack-scenario pairs reach 100% EMR (i.e., misgrounding in every run).
• Scenario-level and stack-level variation is significant. For example, database-migration-gate-decision with Claude Code & Claude Sonnet 4.6 achieves 0% EMR, while most combinations under OpenClaw or OpenCode (with DeepSeek or GLM) are near or at saturation.

Ablation studies indicate:

• Backbone selection plays a primary role: Stack-averaged EMR for Claude Sonnet 4.6 is lowest (55.3%), while DeepSeek-V4-Pro is consistently the highest (mean 95.4% across scaffolds).
• Scaffold choice impacts EMR but is secondary: For shared backbone slices (e.g., Qwen, DeepSeek, GLM with Claude Code, OpenClaw, OpenCode), scaffold means range within roughly 5–10 EMR percentage points, subordinate to backbone variance.
• Open-source scaffolds lack enforceable evidence guardrails: Manual inspection shows that scaffolds typically enforce execution authority for side-effecting actions but not systematic evidence provenance, freshness, or corroboration policy before belief formation. Gating is largely advisory and does not systematically propagate evidence-authority metadata.

Behavioral Analysis

A case study of the low-EMR outlier (Claude Code with Claude Sonnet 4.6 on database-migration-gate-decision) demonstrates a resistance mechanism: the agent inspects downloaded helper artifacts but does not treat them as authorization sources. Instead, it corroborates environment-found claims with trusted authority chains prior to taking irreversible action. This separation of evidence as input (inspectable) versus authority (justification to act) underpins higher reliability; however, such behavior is not seen across most stacks and scenarios.

Limitations

EnvTrustBench focuses on controlled, machine-scoreable cases rather than field-extracted real-world incidents. Scenarios are necessarily narrower than the diversity of actual deployments and exclude dynamic user workflows, organization policies, and interactive user interventions. Evaluation is also limited to a specific slice of available agent implementations and may not generalize without additional feedback-guided scenario construction. Metrics measure observable behavioral failures at the action level, not subjective trust, overall safety, or all indirect harm channels.

Implications and Future Directions

The results decisively demonstrate that evidence-grounding reliability is neither assured by sophisticated LLM architectures nor by modern agent scaffolds. The absence of grounding mechanisms—specifically, mandatory authority and freshness inference, provenance tracking, and systematic corroboration policies—renders agent behavior brittle when exposed to plausible but incorrect or adversarial environmental context.

From a practical standpoint, this highlights the need for layered evidence governance within agentic scaffolds and LLM-agent interfaces. Real-world deployments must consider not only permission gating and prompt-injection hardening but also explicit metadata for source trust, evidence recency, and enforceable verification steps prior to task-significant consequence.

Theoretically, the findings underscore that LLM-agent epistemology—how agents form beliefs from environment-facing observations—remains an open fundamental problem. Ensuring behavioral correctness in the presence of non-authoritative evidence is a prerequisite for secure and reliable agentic automation.

Future research should address:

• Integration of provenance-tracking and authority-verification layers at the scaffold and model levels.
• Automatic detection and mandatory corroboration of mutable or adversarially-supplied environmental claims.
• Construction of more realistic, field-derived EGD scenarios and continuous-case generation pipelines.
• Expansion of validation oracles to encompass longer-horizon or multi-agent settings.

Conclusion

EnvTrustBench provides a rigorous, extensible platform for stress-testing LLM agents on evidence-grounding reliability. Across diverse tasks, models, and scaffolds, EGDs are prevalent, rendering current agent deployments unreliable in adversarial or faulty environments. Essential advances lie in bridging the gap between environmental observation and justified action, embedding systematic evidence governance into agent pipelines, and developing agents that reason robustly under uncertainty regarding external evidence and authority.

For ongoing development in reliable and secure AI agents, these findings reset the focus on epistemic and behavioral provenance rather than solely on action safety or surface-level prompt controls.