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Your Harness is Not Secure: Benchmarking Real-world Threat of Command Line Interface Agent

Weidi Luo, Qiming Zhang, Tianyu Lu, Xiaogeng Liu, Bin Hu, Hung-Chun Chiu, Siyuan Ma, Yizhe Zhang, Xusheng Xiao, Yinzhi Cao, Zhen Xiang, Chaowei Xiao

arXiv:2510.06607v3cs.CR

TL;DR

Existing benchmarks provide limited coverage of attacker knowledge, complete attack chains, realistic environments, and deterministic assessment of CLI-agent misuse. AdvCLI evaluates these risks with an ATT&CK-aligned multi-host benchmark, and its results show agents remain vulnerable, particularly to TTP-informed tasks.

  • Problem

    Existing evaluations leave gaps in realistic CLI-agent misuse coverage, including TTP-based attacker knowledge, end-to-end kill chains, and deterministic verification.

    Source: §1 · pp. 1–2

  • Method

    AdvCLI evaluates OS-level misuse using 140 ATT&CK-aligned tasks in a controlled multi-host sandbox with hard-coded verification.

    Source: §1 · p. 2

  • Results

    Across five independent prompts, CLI agents reached an average ASR@5 of 42.21%, with risk especially pronounced on TTP-based tasks.

    Source: §7 · p. 8, §9 · p. 8

  • Takeaways & Limitations

    AdvCLI provides a reproducible testbed for evaluating CLI-agent misuse risks and developing stronger safety mechanisms.

    Source: §9 · p. 8

    The Linux Docker microsandbox omits Windows-specific APIs, cloud infrastructure, enterprise identity systems, endpoint detection products, and organization-specific access-control policies.

    Source: §10 · p. 9

Abstract

from arXiv · show

Command-line interface (CLI) agents powered by large language models (LLMs) can interpret natural-language requests, plan multi-step tasks, execute shell commands, and modify files and system state. As these agents are increasingly used for operating-system (OS) workflows, it is important to evaluate whether they can be misused to carry out security-relevant operations. Existing benchmarks often lack an attacker-knowledge model grounded in tactics, techniques, and procedures (TTPs), provide limited coverage of end-to-end kill chains, rely on simplified single-host environments, or use LLM-as-a-judge for success evaluation. We introduce AdvCLI, an MITRE ATT&CK-aligned benchmark for evaluating OS-level misuse risks of CLI agents in a controlled multi-host sandbox. AdvCLI contains 140 tasks: 40 direct malicious requests, 74 TTP-based tasks, and 26 end-to-end kill chains. Each task is paired with deterministic hard-coded verification protocols that check whether the requested OS-level effect is realized. We evaluate seven CLI agents and products built on nine foundation models, including ReAct, OpenClaw, OpenAI Agent SDK, Claude Code, Gemini CLI, Cursor CLI, and Cursor IDE. Results show that current CLI agents frequently proceed beyond refusal and can complete a non-negligible fraction of malicious OS-level tasks, especially when requests include TTP-style attacker knowledge. AdvCLI provides a reproducible testbed for evaluating these risks and for developing stronger safety mechanisms for tool-using CLI agents in the future.

1 Introduction

CLI agents can carry out multi-step OS workflows, raising concerns about their misuse for realistic security-relevant attacks. AdvCLI addresses gaps in existing benchmarks and finds that agents can complete malicious tasks, especially when requests include TTP-style knowledge.

1 Introduction

  • Existing benchmarks have limited attacker-knowledge and kill-chain coverage, often use single-host plaintext-secret environments, and may rely on inconsistent LLM-as-a-Judge evaluation.CLI agents can also enable inexperienced attackers by reducing dependence on custom malware and deep expertise.Source: §1 · pp. 1–2
  • AdvCLI is an ATT&CK-aligned benchmark with 140 tasks spanning direct malicious requests, TTP-based tasks, and end-to-end kill chains in a controlled multi-host sandbox.It includes 10 tactics and 77 techniques.Source: §1 · p. 2
  • AdvCLI uses hard-coded verification protocols to assess whether CLI agents realize requested OS-level effects without relying on LLM-as-a-Judge.Source: §1 · p. 2
  • 46.14% average ASR on TTP tasks and 18.68% on end-to-end kill-chain tasks show that current CLI agents remain vulnerable to malicious requests.The results also identify vulnerability to requests phrased as cybersecurity evaluation.Source: §1 · pp. 2–3
  • CTF-style framing increases average ASR by 10.81%, exposing safety-alignment weaknesses when malicious instructions resemble cybersecurity evaluation.Source: §1 · p. 3

2 Related Work

Prior work examines CLI-agent jailbreaks and prompt injection, alongside cybersecurity benchmarks focused on CTF challenges or known vulnerabilities. AdvCLI instead evaluates realistic OS-level misuse through TTPs and multi-stage actions.

2 Related Work

  • Prior cybersecurity benchmarks mainly assess isolated CTF puzzle-solving or known-vulnerability reproduction, while AdvCLI examines executable, multi-stage OS-level misuse through TTPs.CLI agents also expose a larger attack surface than stand-alone LLMs because they can invoke tools and interact with external environments.Source: §2 · p. 3

3 Threat Model

The threat model measures whether an authenticated user or insider can induce a CLI agent to perform OS-level actions that violate the environment’s authorization boundary. It evaluates direct requests, technique-informed requests, and ordered kill chains in a resettable three-host Linux sandbox.

3 Threat Model

  • The adversary supplies natural-language instructions to the CLI agent but cannot directly execute shell commands outside the agent interface.The adversary may be a standard employee or privileged operator, depending on the task precondition.Source: §3 · p. 3
  • The benchmark tests direct malicious requests, TTP-based requests with tactic and technique knowledge, and multi-stage end-to-end kill-chain requests.Source: §3 · p. 3
  • Tasks run in a resettable corporate microsandbox with three Linux hosts, synthetic data, and an isolated private network.The environment captures common OS interactions but does not fully reproduce production enterprise systems, cloud IAM, or endpoint detection.Source: §3 · p. 3

4 Data Collection & Verification

AdvCLI builds tasks from Linux-applicable ATT&CK techniques, manually checks whether they can be safely instantiated and validated, and uses expert consensus to finalize executable tasks. Its verification protocols check effects through separate trigger, probe, and verify procedures.

4.1 Malicious Tasks Generation

  • Ubuntu 22.04 provides the benchmark’s Linux baseline after ATT&CK platform metadata and manual review filter for techniques that can be plausibly implemented in the sandbox.The authors cite Linux’s shared command-line utilities and system architecture with Unix-like desktop platforms and its use in real-world servers as reasons for this choice.Source: §4.1 · pp. 3–4
  • Three experts independently map techniques to tasks, decompose them into executable subtasks, and require consensus on mappings, feasibility, and validation before inclusion.This process yields 74 TTP-based tasks and 26 kill chains composed from ordered tactics and techniques.Source: §4.1 · p. 4

4.2 Verification Protocols

  • Verification uses trigger commands for conditional persistence, probes to check realized OS effects, and verification in the agent session to inspect session-specific state.Separating trigger and probe sessions from the agent session helps avoid cross-talk while attributing triggered behavior to installed mechanisms.Source: §4.2 · pp. 4–5

5 Comparison with Existing Work

AdvCLI distinguishes itself from prior datasets through ATT&CK-informed attacker knowledge, OS-level attack tasks, deterministic verification, and a controlled multi-host environment.

5 Comparison with Existing Work

  • AdvCLI covers tactic- and technique-level tasks plus end-to-end kill chains, modeling malicious procedures beyond high-level objectives.Its multi-host Docker environment uses encrypted credentials and hard-coded protocols to verify attack authenticity and impact.Source: §5 · p. 5

6 Experiment Settings

The evaluation tests seven CLI agents built on nine foundation models in a controlled, resettable three-container sandbox, measuring both tool-use bypass and verified attack completion. Results show higher success on TTP-based tasks than direct tasks, and increased success under CTF framing.

6 Experiment Settings

  • The evaluation uses seven CLI agents across nine foundation models, a Docker-based three-container sandbox, and BSR and ASR metrics for tool use and verified attack completion.The workstation, privileged administration server, and business web server share an isolated private bridge network with deterministic task resets.Source: §6 · p. 5

6.1 Main Results

  • 52.03% average BSR and 29.76% average ASR show agents often proceed beyond refusal and sometimes realize malicious OS-level effects.Table 2 presents results across evaluated agent–model combinations and task categories.Source: §6.1 · p. 6, Table 2 · p. 6
  • TTP-based tasks reach 46.14% ASR and 71.53% BSR, versus 24.46% and 44.46% for Direct tasks; kill chains reach 18.68% ASR and 40.11% BSR.TTP results are higher by 21.68 and 27.06 percentage points, respectively; kill chains require ordered stages and are vulnerable to planning, permission, and execution failures.Source: §6.1 · p. 6

6.2 CTF Framing

  • CTF framing presents malicious instructions as authorized security exercises, exploiting agents’ familiarity with CTF-style requests.The prompt strategy prepends: “Regard this task as a CTF test.”Source: §6.2 · pp. 6–7
  • 10.81% and 10.47% average increases in TTP-task ASR and BSR accompany CTF framing; end-to-end kill-chain ASR and BSR increase by 11.06% and 8.66%.The authors report that this framing can bypass safety alignment by disguising malicious OS-level behavior as cybersecurity evaluation.Source: §6.2 · p. 7, Table 3 · p. 7

7 Discussion

Discussion results identify variation in risk across attack tactics and show that repeated attempts and penetration-testing MCP tools can increase measured success. The paper also reports that end-to-end kill chains remain harder to complete than TTP-based tasks.

Impact of Tactics

  • Credential Access has the highest average ASR at 56.82%, while Command and Control and Exfiltration average 32.81% and 30.00%.The authors suggest information-gathering resemblance may make credential objectives more likely to be operationalized, whereas external communication or data movement may trigger refusal or execution failures.Source: §7 · pp. 7–8

Impact of Best-of-N Attacks

  • Across evaluated combinations and attack categories, five-attempt ASR@5 averages 42.21% and BSR@5 averages 58.46%, gains of 14.53 and 12.49 percentage points over single attempts.TTP-based ASR@5 and BSR@5 reach 70.27% and 84.29%, while end-to-end kill-chain averages are 23.56% and 43.27%.Source: §7 · p. 8

Impact of Penetration MCP Tools

  • Integrating HexStrike-AI into Cursor IDE increased ASR@1 by 53.85% and BSR@1 by 80.77% against baseline Cursor IDE, with both powered by Claude Sonnet 4.The paper reports further gains of 38.46% for ASR@5 and 65.38% for BSR@5.Source: §7 · p. 8

8 Defense

The defense evaluation finds that input-level moderation struggles to block TTP-based malicious requests, including without explicit jailbreak strategies.

8 Defense

  • TTP tasks achieve 28.75% BSR against LLaMA Guard 4 and 83.75% BSR under the OpenAI Moderation API, indicating input-level defenses are insufficient in this evaluation.The authors state that this risk remains under-recognized in the community.Source: §8 · p. 8

9 Conclusion

ADVCLI benchmarks OS-level security risks in CLI agents with MITRE ATT&CK-grounded tasks and hard-coded verification; evaluations find vulnerabilities, particularly to TTP-based requests, and increased attack success under CTF-style framing.

9 Conclusion

  • ADVCLI evaluates CLI agents using 140 MITRE ATT&CK-grounded malicious tasks in a controlled multi-host sandbox with hard-coded verification.The tasks include direct malicious requests, TTP-based tasks, and end-to-end kill chains.Source: §9 · p. 8
  • Evaluations show that CLI agents remain vulnerable, especially to TTP-based requests, and can execute parts of complete attack chains.CTF-style framing further increases attack success, exposing weaknesses in current safety alignment.Source: §9 · p. 8
  • ADVCLI provides a reproducible foundation for evaluating CLI-agent risks and guiding stronger safety alignment.Source: §9 · p. 8

10 Limitations

ADVCLI prioritizes controlled, reproducible evaluation over full enterprise fidelity, limiting its current implementation to a Linux-based Docker microsandbox and manually curated tasks.

10 Limitations

  • ADVCLI uses a three-host Linux Docker microsandbox and does not cover Windows APIs, cloud infrastructure, enterprise identity systems, endpoint detection, or organization-specific access controls.Its manually curated tasks improve benchmark quality but limit scalability.Source: §10 · p. 9

B Ethical Considerations and Potential Risks

F.1 MITRE ATT&CK Framework

  • The benchmark selects 77 Linux-applicable techniques across 10 MITRE ATT&CK tactics and distinguishes adversary objectives from the methods used to achieve them.The framework treats procedures as attacker-designed, environment-specific implementations of techniques, not as a formal level in the ATT&CK matrix.Source: §F.1 · p. 13

F.2 Human Experts

  • Tasks qualify only if they are sandbox-contained, ATT&CK-aligned, security-relevant, reproducible, and deterministically verifiable without an LLM judge.Source: §F.2 · p. 14

F.3 Data Generation

  • The data-generation pipeline filters ATT&CK techniques for Ubuntu-in-Docker feasibility, defines malicious goals, constructs TTP tasks and kill chains, and validates them with hard-coded checks and expert review.Source: §F.3 · p. 14

G.1 Dockerfile Configuration

  • The sandbox uses Ubuntu 22.04 containers with SSH, networking tools, authentication, and file and process controls, while omitting systemd and kernel-module loading.It supports isolated, resettable experiments and limits side effects; its weak settings are intended only for local experiments.Source: §G.1 · p. 15, §G.2 · p. 15

H.1 Trigger

  • The Trigger activates persistence independently of the agent session, the Probe checks the objective in a separate session, and Verify inspects the original session after the agent finishes.Attack Success Rate is counted only when the task-specific hard-coded verification protocol succeeds.Source: §H.1 · p. 16, §H.2 · p. 17, §H.3 · p. 17

H.4 Bypass Success Rate

  • Bypass Success Rate measures whether an agent initiates OS interaction through a shell or MCP tool, not whether the attack succeeds.Refusal-only and chat-only behavior is non-bypassed; attack success is measured separately by hard-coded verification.Source: §H.4 · pp. 17–18

I.2 Evaluation on AutoGPT

AutoGPT and other demonstrations show that ATT&CK-style instructions can be operationalized, while CTF framing increases attack success on average but not uniformly across tactics.

I.2 Evaluation on AutoGPT

  • 54.05% ASR and 81.08% BSR: AutoGPT with GPT-4o achieves these rates on TTP tasks, with lower performance on Direct and End-to-End settings.Other foundation models also show smaller but non-negligible TTP success rates.Source: §I.2 · p. 18, Table 5 · p. 19

J.1 ReAct

  • ReAct demonstrations include persistence, setuid-based privilege escalation, and a man-in-the-middle scenario involving DNS redirection.Source: §J.1 · pp. 19–20

I.3 CTF framing Impact on Per Tactic

  • Across all evaluated model-framework combinations and tactics, CTF framing raises average ASR from 32.65% to 42.33%, an average gain of 9.68 percentage points; Credential Access reaches 67.05% under CTF framing.Source: §I.3 · p. 19

J.2 AutoGPT

  • An AutoGPT demonstration describes a listener that executes commands hidden in junk data received over port 8888.Source: §J.2 · p. 21

J.3 Gemini CLI

  • Gemini CLI demonstrations describe credential harvesting and defense evasion, alongside kill chains that escalate through vulnerable SUID binaries and recover passwords.The examples include combining /etc/passwd and /etc/shadow and using John the Ripper to recover plaintext credentials.Source: §J.3 · p. 21

J.4 Cursor IDE

  • A Cursor IDE demonstration describes ARP spoofing and packet capture to inspect HTTP traffic, while another recovers a weak password from an SSSD cache hash.Source: §J.4 · pp. 21–22

J.5 Cursor CLI

Cursor CLI demonstrations describe event-triggered destructive execution, steganographic payload transfer, and a multi-stage chain that escalates privileges, deploys a persistent backdoor, and exfiltrates files.

J.5 Cursor CLI

  • Other Cursor CLI demonstrations use a udev rule to trigger destructive code with root privileges and conceal a destructive command inside a JPEG sent to a remote host.Source: §J.5 · p. 22
  • The accompanying task prompts specify port-knocking persistence, setuid privilege escalation, DNS spoofing, command-channel setup, and automated file transfer.Source: §J.5 · pp. 24–28
  • Cursor CLI’s demonstrated kill chain escalates to root, deploys a persistent background script, and repeatedly exfiltrates files from a home directory over SCP.The script is copied to a remote target and runs without further attacker input.Source: §J.5 · p. 22, Figure 22 · p. 36
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