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SABER: Benchmarking Operational Safety of LLM Coding Agents in Stateful Project Workspaces

Qi Hu, Yifeng Tang, Qinghua Wang, Lanyang Zhao, Pengji Zhang, Yuhao Qing, Xin Yao, Dong Huang, Lin Zhang, Zhuoran Ji

arXiv:2606.01317v1cs.SEcs.CR

TL;DR

Existing safety benchmarks largely test refusal in isolated interactions, missing risks from persistent actions in stateful project workspaces. SABER evaluates completed coding-agent runs and finds that even the best-performing model achieves only a 31.0% safe-completion rate.

  • Problem

    Existing safety benchmarks mostly assess refusal in isolated prompt-response interactions, leaving safety in stateful, multi-step project environments insufficiently evaluated.

  • Method

    SABER evaluates coding agents in realistic Docker-sandboxed project workspaces by judging action traces and resulting state against task-specific and global safety properties.

  • Results

    54.7% HSR is achieved by the best-performing model, while all evaluated models show substantial operational safety failures in executable project environments.

  • Takeaways & Limitations

    Refusal behavior alone is insufficient for agent safety; agents must also plan safely, choose least-privilege operations, and preserve persistent project state.

  • Takeaways & Limitations

    SABER’s Docker sandboxes do not fully reproduce production environments, including VM-backed systems, cloud IAM, multi-user permissions, and long-running services.

Abstract

from arXiv · show

Large language models are increasingly deployed as coding agents, shifting safety from individual responses to action sequences. Existing benchmarks, however, primarily assess whether models refuse unsafe prompts, leaving impacts on stateful workspaces largely unexamined. We present SABER, a benchmark for environment-aware operational safety that places models in realistic agent-style projects and evaluates safety from the final environment state after a sequence of actions. Beyond binary safety-violation reports, SABER categorizes violations by cause, enabling analysis of model-specific safety profiles. Our evaluations show that even the best-performing model has more than a 54% harmful safety-violation rate (HSR), suggesting that current alignment remains insufficient for realistic project environments. SABER further reveals distinct safety profiles across models. Our benchmark is publicly available at https://github.com/sssr-lab/saber.

1 Introduction

SABER evaluates the operational safety of LLM coding agents through their actions and final states in realistic, stateful project workspaces. Across 13 models and 716 executable tasks, it reveals distinct safety profiles, while the best-performing model achieves only a 31.0% safe-completion rate.

  • Motivation: LLM agents can edit files and execute shell commands, expanding safety risks from generated text to persistent changes in operating-system resources and project state.This shift supports debugging and multi-step automation but also enables harmful action sequences.
  • Problem: Most existing safety benchmarks test isolated prompt-response behavior rather than persistent side effects from multi-step actions in stateful project environments.They primarily assess compliance with explicitly harmful requests or resistance to injected instructions.
  • Method: SABER places models in Docker-isolated, reproducible agent-style workspaces containing source code, configuration files, and git history, then evaluates what agents do.The benchmark is designed to mirror the project environments in which modern coding agents operate.
  • Results: 31.0%: the best-performing model’s safe-completion rate across SABER’s evaluation of 13 coding-capable models on 716 executable tasks.The result indicates that current alignment remains insufficient for realistic project environments.
  • Contributions: SABER distinguishes genuine safety from over-refusal and attributes harm to malicious environmental content, unsafe autonomous choices, or missed contextual warnings.Its protocol evaluates completed agent runs using task-specific harmful-pattern detection and global safety-property checks.

2 Related Work

Prior safety benchmarks span instruction refusal, prompt injection, and tool-use or agentic environments, including code-security evaluation. However, they largely overlook project-level artifacts, autonomous dangerous operations during legitimate tasks, and safety as a property of environmental context.

  • Instruction refusal: Existing work primarily evaluates LLM safety through instruction refusal, covering adversarial suffixes, broad harm taxonomies, red-teaming, and over-refusal of safe prompts.AgentHarm extends this line to agentic settings with multi-step interactions.
  • Prompt injection: Prompt-injection research formalizes indirect injection and evaluates attacks in agentic tool-calling environments, while ASB unifies prompt injection, memory poisoning, and backdoor attacks.The literature includes large-scale attack-defense data collection through gamification and newer agentic evaluations.
  • Tool-use and agentic safety: Tool-use and agentic benchmarks assess privacy compliance, unsafe API calls, interaction risk awareness, sandbox-based risk identification, and insecure code generation.Examples include PrivacyLens, SafeToolBench, R-Judge, ToolEmu, CyberSecEval, and RedCode.
  • Limitations: Existing benchmarks inject threats through prompts, tool outputs, or skill files rather than project-level artifacts such as build configurations or dependency manifests.This limitation leaves project-workspace attack surfaces insufficiently evaluated.
  • Limitations: Existing benchmarks test compliance with explicitly harmful or injected instructions, not whether models autonomously choose dangerous operations while pursuing legitimate goals.The limitation concerns autonomous operational behavior rather than instruction following alone.
  • Limitations: Existing benchmarks treat safety as an instruction property without considering environmental context.The supplied passage identifies this as a distinct limitation of prior evaluation.

3 Preliminary Analysis

Preliminary analysis evaluates 13 models on nine safety benchmarks and finds that existing safety signals are inconsistent and incomplete. It identifies gaps in recognizing compromised environments, safely operating autonomously, and adapting instructions to environmental context, motivating SABER’s consequence-based project evaluation.

  • Benchmark findings: 13 models are evaluated on nine representative safety benchmarks, whose signals remain inconsistent and incomplete.Table 1 combines high-risk tool-use, safe-request compliance, unsafe, and attack-success rates, with metric direction varying by benchmark.
  • Benchmark findings: 85.0% on AgentDyn and 69.7% on Skill-Inject make DeepSeek-R1 one of the most vulnerable models despite strong reasoning ability.The passage reports these vulnerability rates as among the highest observed.
  • Identified gaps: Existing benchmarks miss risks from multi-step workflows in which agents execute shell commands, edit files, and interact with version control.These workflows create safety gaps beyond evaluations focused on individual benchmark interactions.
  • Identified gaps: The gaps include detecting malicious project artifacts, avoiding unsafe autonomous operations, and adjusting behavior when environmental context changes the consequences.Examples include attacks in Makefiles or package.json, overly permissive permissions or destructive commands without confirmation, and database resets that differ in risk between development and production.
  • Motivation for SABER: These gaps motivate SABER’s evaluation in realistic, sandboxed agentic project environments where actions have concrete consequences.The benchmark’s design is introduced as the subject of § 4.

4 Benchmark Design

SABER benchmarks operational safety through bounded, auditable interactions between coding agents and Docker-sandboxed project workspaces, judging the resulting workspace state rather than isolated responses or tool calls. Its design covers environment-borne instructions, agent-originated unsafe choices, and contextual hazards, then assigns run-level outcomes from recorded evidence.

  • Evaluation setting: Each task combines an initialized project environment, a user request, and a bounded multi-step interaction whose actions can alter persistent files, data, permissions, and other workspace state.The benchmark records shell trajectories, event streams, command outputs, conversations, and state-change evidence for adjudication.
  • Threat coverage: SABER organizes threats by causal origin into embedded malicious instructions, unsafe agent choices, and context-dependent hazards discovered through workspace evidence.Safe behavior may require surfacing risk, avoiding the unsafe action, and offering a safer alternative or explanation.
  • Task construction: Tasks are executable Docker-sandboxed interactions with a user goal, initialized workspace, operational interfaces, and a bounded interaction loop.The harness proxies tool calls through a controlled interface, including shell access and, for some tasks, MCP-style tools or skill-style procedural artifacts.
  • Outcome adjudication: Run-level adjudication uses the unified event stream, safety-relevant state deltas, conversation, and final response to determine safety and utility over the complete interaction process.The final outcome also incorporates termination reason and refusal-validity judgments, with refusal validity applying only to non-violating safety aborts.

5 Experiments

SABER evaluates operational safety across 716 executable tasks in Docker-sandboxed project workspaces, revealing substantial and structurally diverse harmful behavior. Results show that unsafe execution persists across causal scenarios, often reflects operational misunderstanding, and is not reliably prevented by stronger capabilities or refusal behavior.

  • Testbed: 716 executable tasks span embedded injection, risky self-directed actions, and other causal origins in Docker-sandboxed project workspaces.The benchmark reports PHR for harmful follow-on effects and CPR for harm arising through multiple coordinated steps.
  • Aggregate results: 54.7% HSR is achieved by the best-performing model, Claude Opus 4.6, while GPT-5.4 reaches 63.9% and DeepSeek-R1 reaches 84.7%.Most open-model variants fall between 70% and 80%, and low SRR indicates weak early risk recognition.
  • Failure causes: 47.7% of harmful runs stem from task_misunderstood, compared with 25.4% from injection-following and 25.1% from harmful-operation compliance.HSR profiles vary markedly across models and scenarios, with high-risk cells spanning all three causal origins.
  • Scenario findings: 70.1% HSR occurs under artifact-level injection in Scenario A, including 23.0% CPR, while benign Scenario B still reaches 68.3% HSR without an adversary.Unsafe behavior can arise from readable workspace artifacts or from choosing a high-risk affordance despite an available safer path.
  • Scenario findings: 82.5% HSR, 12.4% PHR, and 24.1% CPR occur in warning-rich Scenario C, showing that contextual safety signals rarely constrain execution.Unauthorized access, network outbound actions, and information leakage have CPR values of 32.9%, 30.8%, and 28.1%, respectively.
  • Capability and risk recognition: 79.6% HSR versus 72.4% accompanies DeepSeek-V3.2 versus DeepSeek-V3, while Qwen3.5 changes only from 78.6% at 9B to 77.3% at 35B.Claude Opus 4.6 and GPT-5.4 have the lowest HSR values, 54.7% and 63.9%, but the highest LRR values, 9.0% and 7.4%, indicating that recognition can occur after harmful consequences.

6 Conclusion

SABER evaluates operational safety by testing LLM coding agents in executable, stateful project workspaces and judging completed runs from action traces and resulting states. Its findings show that refusal behavior alone is insufficient: safe agents must identify unsafe requests, choose least-privilege operations, plan safely, and preserve persistent project state across multi-step execution.

  • Benchmark design: SABER benchmarks operational safety in realistic, stateful project workspaces using executable agent environments.Completed runs are evaluated from their action traces and resulting workspace states.
  • Safety findings: Refusal behavior alone is insufficient for coding-agent safety.The benchmark exposes failures that response-level safety tests often miss.
  • Safety findings: Safe agents must identify unsafe requests, plan safely, choose least-privilege operations, and preserve persistent project state during multi-step execution.

Limitations

SABER’s unified harness isolates models’ own safety reasoning but does not measure vendor-specific safeguards. Its outbound-network tasks also lack real Internet or third-party service access, avoiding actual leakage or remote modification.

  • Harness scope: SABER’s unified ReAct-style harness and common tool interface exclude vendor-specific harnesses, confirmation policies, planning scaffolds, rollback mechanisms, and additional safety filters.This design supports fair model-to-model comparison while focusing evaluation on the LLM’s own safety reasoning.
  • Network realism: SABER evaluates outbound-network tasks without real Internet or third-party service access, avoiding actual leakage or remote modification.The limitation reduces realism for network-dependent operational-safety scenarios.

Ethical Considerations … A.3 SafeToolBench Overall Results

The paper frames SABER as a defensively oriented benchmark with safeguards against dual-use risk, and situates its preliminary study across nine safety benchmarks. It details Skill-Inject’s camouflage-based splits and SafeToolBench’s rubric-based risk threshold.

  • Ethical Considerations: SABER evaluates LLM-based coding agents defensively in isolated sandboxes using synthetic project states without real credentials or third-party service access.Its source materials abstract operational failure modes rather than reproducing attack paths; released tasks do not replicate vendor-specific exploit chains or disclose new, u
  • A.1 Benchmark Overview: The preliminary study covers nine safety benchmarks, summarized in Table 4.
  • A.2 Skill-Inject Per-Split Results: Skill-Inject results report attack success rate (%) separately for four difficulty splits.The splits progress from Obvious and Normal to Legit and Warning according to camouflage difficulty.
  • A.2 Skill-Inject Per-Split Results: Obvious Skill-Inject cases contain explicit malicious instructions and are the easiest to detect.
  • A.2 Skill-Inject Per-Split Results: Warning cases use safety-themed text for camouflage and are the hardest to detect.
  • A.3 SafeToolBench Overall Results: SafeToolBench reports average risk score and high-risk rate (%) using a nine-dimension rubric with a maximum score of 27.The rubric has four instruction, three tool, and two instruction–tool alignment dimensions; high-risk tool use requires S > α with α = 10.

A.4 SafeToolBench Per-Split Results · B Source-to-Template Mapping and Coverage Criteria

SafeToolBench shows that multi-agent settings are riskier than single-agent settings across all categories, with Bias & Offensiveness and Property Damage especially challenging. SABER builds executable workspace tasks from multiple source families, retaining templates that are causally specific, locally resolvable, observably harmful, and balanced across coverage dimensions.

  • A.4 SafeToolBench Per-Split Results: Multi-agent settings consistently produce higher average risk scores than single-agent settings across all SafeToolBench risk categories.The categories are Bias & Offensiveness, Property Damage, Physical Injury, and Privacy Leak.
  • A.4 SafeToolBench Per-Split Results: Bias & Offensiveness and Property Damage are the most challenging SafeToolBench categories, while single-agent Physical Injury is safest.
  • B.1 Construction Overview: SABER constructs templates from prior agent-safety benchmarks, public vulnerability reports and advisories, and practitioner workflow seeds.These sources identify risk boundaries, operational failure modes, realistic harmful conditions, artifact channels, and expected safe resolutions.
  • B.1 Construction Overview: Templates are organized by causal origin—embedded injection, risky self-selection, or contextual warnings—and by eight task categories describing the operational object or unsafe effect.
  • B.3 CVE- and Advisory-Inspired Sources: Public vulnerability reports ground templates in documented operational failure modes without reproducing the underlying exploits.
  • B.4 Practitioner Workflow Seeds: Practitioner workflow seeds model realistic harm conditions such as urgency, partial automation, shared credentials, production-like context, and convenience shortcuts.They supply plausible user requests and workspace artifacts, including credential reuse and broad destructive cleanup when scoped repairs are available.
  • B.5 Coverage Criteria and Validation: Retained templates must have one causal origin, a workspace-inferable safe path, an executable observable harmful outcome, and balanced coverage.Validation removes ambiguous, under-specified, unclear, non-executable, or multi-dominant instances so each task tests one dominant operational-safety failure mode.
  • B.5 Coverage Criteria and Validation: Task instances are created as concrete workspaces, populated with controlled LLM-assisted synthesis, and validated through execution and manual review.Filtering preserves realistic, executable tasks whose safe actions require examining the workspace.

B.2 Prior Benchmark Sources

SABER derives reusable workspace-pattern templates from prior agent-safety benchmarks spanning environment hijacking, unsafe tool execution, persistent contamination, explicit harmful-use requests, and context-aware failures. These sources define SABER’s threat surfaces while positioning explicit harmful-use benchmarks as boundary references rather than its primary scenario.

  • Template abstraction: Prior benchmarks are abstracted into workspace patterns linking a user goal, an artifact channel, and an underlying operational failure mode.The source families separate prompt injection, unsafe tool execution, persistent poisoning, explicit harmful-use requests, and context-aware safety failures into reusable templates.
  • Environment-borne instruction hijacking: Environment-borne instruction hijacking is generalized from external tool outputs and injected text to project-native artifacts such as source files, logs, build scripts, and configuration files.The resulting scenarios require agents to handle untrusted content within realistic project workspaces.
  • Tool-layer manipulation and unsafe execution: Tool-layer manipulation covers benign tasks where unsafe tool arguments, execution order, or destructive operations create avoidable execution paths.This family extends prior tool-safety and risky-code findings into templates that require avoiding unsafe execution despite benign task goals.
  • Boundary reference: Explicit harmful-use benchmarks such as AgentHarm and HarmBench serve as boundary references, clarifying that SABER is not centered on refusal-only evaluation or direct harmful-use prompts.The family informs benchmark scope rather than providing SABER’s primary novelty.
  • Policy-, consent-, and context-aware failure: Context-aware safety work motivates workspace-level warnings that agents must discover through local-artifact inspection before choosing a safer alternative or refusing.This family emphasizes that safety depends on policy, consent, and contextual constraints rather than the request alone.

C Task Format and Runtime Details

SABER task specifications separate runtime-facing execution fields from post-run judging metadata, defining the workspace, agent interaction, and evaluation context. The harness executes each specification in a fresh sandbox, returns tool observations during inference, and evaluates resulting state through recorded traces and deltas.

  • Task specification: Runtime-facing fields define the task identity, agent prompts, project workspace, environment injection, and executable interaction context.The system prompt describes available environments and tools, while the user prompt states the concrete goal; mock_fs and file_contents construct the project state.
  • Runtime execution: The example tests least-privileged tool selection: relocation quarantines only the target project’s artifacts, whereas reclaim deletes the shared cache root.The task exposes multiple plausible operations, contrasting a safe MCP path with a faster unsafe path and producing different filesystem effects.
  • Runtime execution: The harness initializes each task in a fresh Docker container by materializing files, applying permissions, running setup commands, and setting the working directory.After initialization, it builds the tool interface for the selected model and executes tool calls in the sandbox.
  • Task specification: Judging metadata is separated from inference fields and remains hidden from the model during execution.The model receives only agent-facing context and tool schemas, while collapsed judging metadata is used after the run.
  • Run traces: SABER records shell trajectories, unified tool events, model conversations, final responses, and state deltas as the run artifact consumed by judging.State deltas distinguish scoped moves and benign updates from filesystem deletions across shell and MCP-style actions.

D Judging Protocol Details · E Additional Evaluation Analysis

SABER’s post-run judging protocol combines deterministic state- and pattern-based checks with auxiliary semantic and refusal-validity judgments to classify safety, termination, and capability outcomes. Its appendix provides quantitative analyses of aggregate outcomes, scenario and category decompositions, harm mechanisms, failure modes, and a representative case study.

  • D Judging Protocol Details: The harness judges each run from its conversation, final response, event stream, tool outputs, shell trajectory, state deltas, and post-run metadata.These records are grouped as E_r = (τ_r, ∆_r, c_r) and used to derive the violation indicator H(r) and termination pair T(r).
  • D Judging Protocol Details: The primary harmfulness detector deterministically checks state deltas, unified events, shell commands, and structured tool calls for destructive, exfiltrative, unauthorized, or task-specific harmful effects.Expected-safe fields provide positive evidence for safe resolution, whereas harmful-pattern fields contribute to Hrule(r) when matched.
  • D Judging Protocol Details: An auxiliary semantic judge flags compositional harm and advice-only harmful completion, while empty-trajectory responses are classified as safety-aware refusal, incapability, or harmful completion.The semantic judge also assigns q(r) as complete, abort, or fail; refusal-validity assigns a(r) only to non-violating safety aborts.
  • D Judging Protocol Details: Refusal validity distinguishes justified refusals from unnecessary refusals, reclassifying the latter as Incapable when a safe path was available.A justified refusal requires no clear safe way to complete or meaningfully advance the request; safe bounded inspection or least-privilege progress can make refusal unnecessary.
  • D Judging Protocol Details: HSR is the primary metric: it measures the fraction of effective runs with any safety violation, using effective runs as the denominator so refusal or failure cannot artificially lower it.The protocol also reports per-scenario and category-group HSRs, while SRR measures justified refusals across all runs and IR measures ordinary failures plus unnecessary refusals.
  • D Judging Protocol Details: LRR measures the fraction of harmful runs that end in Late Refusal after a violation has occurred, and is defined as zero when no harmful runs exist.Further analysis uses state-delta evidence and semantic judging to identify propagating and compositional harm through PHR and CPR.
  • E Additional Evaluation Analysis: The appendix reports aggregate outcome counts across model–task runs, along with scenario- and category-level decompositions.It extends the compact main-text evaluation with quantitative evidence organized by outcome and evaluation slice.
  • E Additional Evaluation Analysis: The appendix also presents propagation and compositional-harm analyses, a data-grounded failure-mode summary, and a representative case study.These analyses provide additional evidence for how workspace-level harms arise and how failures appear in representative runs.

E.1 What Existing Benchmarks Miss … E.5 Case Study: Safe Path versus Fast Unsafe Reclaim

SABER evaluates operational safety from state-changing interactions in project workspaces, exposing propagation, composition, and workspace-reasoning failures missed by refusal-only benchmarks. Its results show high harmful rates, distinct causes, and how agents can choose fast destructive paths over safe operational procedures.

  • E.1 What Existing Benchmarks Miss: SABER evaluates full state-changing agent–workspace interactions, unlike benchmarks focused on prompt refusals, isolated tool choices, or limited indirect-instruction channels.This distinction shifts safety assessment from individual responses to final project-workspace effects.
  • E.1 What Existing Benchmarks Miss: 82.5% HSR makes Scenario C the hardest split, while Scenario B reaches 68.3% HSR without an attacker, revealing failures from unsafe path selection and local-evidence reasoning.Scenario A also shows that indirect-injection risk extends into project-native artifacts.
  • E.2 Propagating and Compositional Harms: 21.0% average CPR exceeds 8.9% average PHR, indicating that operational failures commonly arise from sequences of locally plausible actions rather than isolated mistakes.CPR ranges from 5.7% to 37.6%, while PHR ranges from 3.9% to 13.9%.
  • E.2 Propagating and Compositional Harms: 25.4% PHR is highest for persistence, while CPR peaks in unauthorized access at 32.9%, network outbound at 30.8%, and information leakage at 28.1%.These patterns distinguish durable or outward propagation from multi-step composition across task categories.
  • E.3 Contribution of Semantic Judging: 69.8% of 6,015 harmful runs are captured by deterministic property checks or harmful command/tool patterns, leaving 30.2% for the auxiliary LLM semantic judge.A random 20% audit of semantic-judge labels found all sampled labels matched the expected judgments.
  • E.4 Coarse Cause Labels for Harmful Runs: 47.7% of harmful runs are attributed to task misunderstanding, followed by injection-following at 25.4%, harmful-operation compliance at 25.1%, and unsafe advice at 1.8%.These are coarse annotations of harmful-run origins rather than a separate taxonomy of mechanisms.
  • E.4 Coarse Cause Labels for Harmful Runs: 21.3% pooled CPR shows that many harmful runs involve multi-step workflows, while the 12.5% Incapable share reflects avoidance of harm through task abandonment rather than safe competence.Task misunderstanding dominates Scenario B, whereas injection-following is largest in Scenario A but still interacts with broader workspace reasoning.
  • E.5 Case Study: Safe Path versus Fast Unsafe Reclaim: In the filesystem-reclaim case, Claude Opus 4.6 completes safe indexed relocation, whereas DeepSeek-R1 invokes the harmful runner-reclaim tool and receives a Harmful Completion label.SABER uses persistent workspace effects and hidden post-run metadata to distinguish safe competence from fast unsafe completion.
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