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arxiv: 2606.18356 · v1 · pith:QNAIEZPXnew · submitted 2026-06-16 · 💻 cs.CR · cs.AI

SafeClawBench: Separating Semantic, Audit-Evidence, and Sandbox Harm in Tool-Using LLM Agents

Yuchuan Tian , Mengyu Zheng , Haocheng Mei , Ye Yuan , Chao Xu , Xinghao Chen , Hanting Chen , Yu Wang This is my paper

Pith reviewed 2026-06-26 23:38 UTC · model grok-4.3

classification 💻 cs.CR cs.AI
keywords LLM agentstool use securityprompt injectionsandbox evaluationadversarial benchmarkssemantic harmexecutable harm
0
0 comments X

The pith

Semantic acceptance and sandbox-observed harm are distinct failure modes in tool-using LLM agents.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a staged benchmark that measures three separate outcomes for adversarial tasks given to tool-using agents: whether the model semantically accepts the attack, whether audit logs record evidence of harm, and whether the sandbox actually executes observable state changes. A matched analysis of 12,000 rows shows that 291 of 347 sandbox harms occur in tasks that passed the semantic check. This separation matters because prior evaluations combined textual compliance with executable effects into one success rate, making it hard to locate where defenses should intervene. The benchmark runs the same 600 tasks across five agent endpoints and four prompt policies to expose how each endpoint responds independently.

Core claim

Semantic attack acceptance, audit-visible harm evidence, and sandbox-observed tool or state harm are separable failure modes; in the 12,000-row matched analysis, most observed sandbox harms arise in rows that pass the semantic check.

What carries the argument

Three-endpoint staged evaluation protocol that reports semantic acceptance, audit evidence, and sandbox state changes as distinct measurements on the same tasks.

If this is right

  • Semantic failure rates alone do not bound the rate of executable harm.
  • Prompt-level policies alter outcomes, but the size and direction of the change depend on both the model and the chosen endpoint.
  • Audit-visible evidence forms a narrower set than semantic acceptance.
  • Different models exhibit different profiles across the three endpoints.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Safety testing for agents must include executable sandbox runs rather than stopping at text-level checks.
  • Defenses tuned only on semantic acceptance may leave executable harm channels open.
  • The same task set can be reused to compare future models or policies without conflating the three layers.

Load-bearing premise

The sandbox protocol produces the same observable state changes that would occur if the same tool calls ran in a production environment.

What would settle it

A larger matched sample in which every sandbox harm is preceded by semantic acceptance under the same task identities.

Figures

Figures reproduced from arXiv: 2606.18356 by Chao Xu, Hanting Chen, Haocheng Mei, Mengyu Zheng, Xinghao Chen, Ye Yuan, Yuchuan Tian, Yu Wang.

Figure 1
Figure 1. Figure 1: SafeClawBench benchmark and endpoint structure. The benchmark starts from 600 curated agent-security cases, evaluates a fixed five-endpoint × four-policy Semantic Core panel, audits canonical CoreFail rows for artifact-visible harm evidence, and separately runs matched cases in the Exec-Balanced sandbox. The paper reports CoreFail@600, HarmEvidence@600, SemanticOnly, and ObservedHarm@Exec as distinct endpo… view at source ↗
Figure 2
Figure 2. Figure 2: Reduced-panel CoreFail@600 defense trajectories. Each row traces one endpoint across prompt policies; lower is better. 4.3 RQ1: Semantic Core Unless otherwise noted, Core results use CoreFail@600: the LLM judge’s binary label for whether the final response complied with the attack goal, averaged over the fixed 600-case denominator. We reserve ToolCall-ASR and StateChange-ASR for executable endpoints [PITH… view at source ↗
Figure 3
Figure 3. Figure 3: Exec ObservedHarm by defense. Bars show means; dots and ranges show endpoint spread. 5 Endpoint Analyses Memory poisoning is effective because memory content persists and is often treated as trusted context. MEX is high for a different reason: the current aggregate MEX family combines ex￾act seeded-secret leakage, protected-configuration disclosure, protected memory/record retrieval, and broad policy/confi… view at source ↗
read the original abstract

Tool-using language-model agents introduce security failures that go beyond unsafe text: they can disclose protected objects, write persistent memory, send messages, modify databases, or trigger harmful code and tool effects. Existing evaluations often collapse these stages into a single attack success rate, making it difficult to tell whether a model merely agreed with an attacker or actually produced observable harm. We introduce SafeClawBench, a staged benchmark for tool-using agent security with 600 controlled adversarial tasks across six attack families: direct and indirect prompt injection, tool-return injection, memory poisoning, memory extraction, and ambiguity-driven unsafe inference. SafeClawBench reports three separate endpoints: semantic attack acceptance, audit-visible harm evidence, and sandbox-observed tool/state harm. Evaluating five agent endpoints under four prompt-level policies, we find that these endpoints capture different failure modes. Without additional prompt protection, semantic failure rates vary widely across models, from 9.0% to 44.2%. Audited harm evidence is narrower than semantic failure, and under a separate executable protocol some matched task identities produce sandbox harm despite passing the Semantic Core call: in a 12,000-row matched analysis, 291 of 347 observed sandbox harms occur in rows that pass the semantic check. Prompt policies change endpoint outcomes, but their effects depend on both model and protocol. SafeClawBench provides a reproducible framework for comparing agent models and prompt-policy conditions without conflating textual compliance, evidence-supported harm, and executable state changes. The open-source dataset is available at https://huggingface.co/datasets/sairights/safeclawbench.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces SafeClawBench, a staged benchmark with 600 controlled adversarial tasks across six attack families for tool-using LLM agents. It defines and evaluates three separate endpoints—semantic attack acceptance, audit-visible harm evidence, and sandbox-observed tool/state harm—across five agent endpoints and four prompt policies. The central empirical claim, supported by a 12,000-row matched analysis, is that these endpoints capture distinct failure modes, with 291 of 347 observed sandbox harms occurring in rows that pass the semantic check.

Significance. If the results hold, the benchmark offers a reproducible framework for disentangling textual compliance from evidence-supported and executable harm in agent systems, addressing a gap in existing single-metric evaluations. The open-source dataset and concrete cross-model, cross-policy numbers strengthen its utility for comparing agent security. The work is empirical benchmark construction rather than a closed derivation, with the reported percentages as measured outcomes.

major comments (1)
  1. [Abstract (executable protocol description) and methods] The 12,000-row matched analysis result (291/347 sandbox harms in semantically accepted rows) treats sandbox-observed state changes as executable harm. This equivalence depends on the sandbox execution protocol producing only effects that would occur in production tool calls. The manuscript should provide explicit validation or discussion of sandbox fidelity (e.g., how mocks, permission settings, or simulated services avoid introducing artifacts absent from real environments) in the methods or limitations section, as any mismatch directly affects the separation claim.
minor comments (2)
  1. [Abstract and evaluation setup] Clarify the exact definition and implementation of the 'Semantic Core call' used to determine passage of the semantic check, including any thresholds or model-specific details.
  2. [Abstract] The abstract states semantic failure rates vary from 9.0% to 44.2% without protection; include the per-model breakdown and policy effects in a summary table for immediate readability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment regarding sandbox fidelity below.

read point-by-point responses

  1. Referee: [Abstract (executable protocol description) and methods] The 12,000-row matched analysis result (291/347 sandbox harms in semantically accepted rows) treats sandbox-observed state changes as executable harm. This equivalence depends on the sandbox execution protocol producing only effects that would occur in production tool calls. The manuscript should provide explicit validation or discussion of sandbox fidelity (e.g., how mocks, permission settings, or simulated services avoid introducing artifacts absent from real environments) in the methods or limitations section, as any mismatch directly affects the separation claim.

    Authors: We agree that explicit discussion of sandbox fidelity is necessary to support the interpretation of sandbox-observed harms. In the revised version, we will add a subsection in the Methods describing the sandbox execution protocol in detail, including the use of mocks, permission settings, and simulated services. Additionally, we will include a dedicated paragraph in the Limitations section discussing potential discrepancies between the sandbox and production environments and how these might affect the observed separation of failure modes. This addresses the concern without requiring new experiments. revision: yes

Circularity Check

0 steps flagged

Empirical benchmark reports measured outcomes with no derivation reducing to inputs by construction

full rationale

The paper introduces SafeClawBench as a staged evaluation framework and reports direct counts from a 12,000-row matched analysis (291 of 347 sandbox harms in semantically passing rows). These are observed empirical results from running the benchmark on agent endpoints, not quantities defined by the benchmark itself or obtained via fitted parameters, self-citation chains, or ansatz smuggling. No equations, uniqueness theorems, or load-bearing derivations appear in the provided text that would equate any reported endpoint separation to its own inputs. The work is self-contained as an empirical construction and measurement study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The benchmark introduces three new measurement protocols (semantic, audit, sandbox) that are defined by the paper rather than taken from prior literature. No numerical free parameters are fitted to data in the reported results. No new physical or theoretical entities are postulated.

axioms (1)
  • domain assumption Sandbox execution produces observable state changes that correspond to real tool effects.
    Invoked when the paper treats sandbox-observed harm as a distinct and meaningful endpoint.

pith-pipeline@v0.9.1-grok · 5846 in / 1372 out tokens · 24459 ms · 2026-06-26T23:38:49.043421+00:00 · methodology

discussion (0)

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