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Jailbreaking LLM-Controlled Robots

Alexander Robey, Zachary Ravichandran, Vijay Kumar, Hamed Hassani, George J. Pappas

arXiv:2410.13691v2cs.ROcs.AI

TL;DR

LLM-controlled robots introduce a safety gap because chatbot jailbreaks can be transformed into harmful physical actions. The paper develops RoboPAIR to test this risk across white-box, gray-box, and black-box systems, often achieving 100% attack success rates. These findings establish the need for robot-specific defenses, while the evaluated systems still require targeted prompting and, for two robots, physical access.

  • Problem

    The robustness of LLM-controlled robots against jailbreaks is poorly understood, despite their potential to cause physical harm.

    Source: §1 · pp. 2, 4

  • Method

    RoboPAIR adapts PAIR with robot-specific prompts, harmful-action examples, and a syntax checker for executable robot code.

    Source: §1 · p. 3, §4.1 · p. 7, §4.3 · p. 8

  • Results

    RoboPAIR finds jailbreaks quickly across three attack scenarios, often achieving 100% attack success rates.

    Source: §1 · p. 3

  • Takeaways & Limitations

    Jailbroken LLM-controlled robots can pose physical safety risks, so safe deployment requires robot-specific defenses.

    Source: §1 · p. 4, §8 · p. 20

    The evaluated robots lack sufficient situational awareness for harmful actions without targeted prompting and specific contexts, while the Jackal and Go2 attacks require physical access.

    Source: §7 · p. 19

Abstract

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The recent introduction of large language models (LLMs) has revolutionized the field of robotics by enabling contextual reasoning and intuitive human-robot interaction in domains as varied as manipulation, locomotion, and self-driving vehicles. When viewed as a stand-alone technology, LLMs are known to be vulnerable to jailbreaking attacks, wherein malicious prompters elicit harmful text by bypassing LLM safety guardrails. To assess the risks of deploying LLMs in robotics, in this paper, we introduce RoboPAIR, the first algorithm designed to jailbreak LLM-controlled robots. Unlike existing, textual attacks on LLM chatbots, RoboPAIR elicits harmful physical actions from LLM-controlled robots, a phenomenon we experimentally demonstrate in three scenarios: (i) a white-box setting, wherein the attacker has full access to the NVIDIA Dolphins self-driving LLM, (ii) a gray-box setting, wherein the attacker has partial access to a Clearpath Robotics Jackal UGV robot equipped with a GPT-4o planner, and (iii) a black-box setting, wherein the attacker has only query access to the GPT-3.5-integrated Unitree Robotics Go2 robot dog. In each scenario and across three new datasets of harmful robotic actions, we demonstrate that RoboPAIR, as well as several static baselines, finds jailbreaks quickly and effectively, often achieving 100% attack success rates. Our results reveal, for the first time, that the risks of jailbroken LLMs extend far beyond text generation, given the distinct possibility that jailbroken robots could cause physical damage in the real world. Indeed, our results on the Unitree Go2 represent the first successful jailbreak of a deployed commercial robotic system. Addressing this emerging vulnerability is critical for ensuring the safe deployment of LLMs in robotics. Additional media is available at: https://robopair.org

1 Introduction

LLMs are increasingly embedded in robots, but their safety alignment remains vulnerable to jailbreaking that can elicit harmful physical actions rather than harmful text. This paper introduces RoboPAIR, evaluates it across three access settings, and finds frequent 100% attack success rates, motivating robot-specific defenses.

Motivation

  • LLM adoption is expanding across robotic control, contextual reasoning, self-driving, and human-robot interaction, including commercially available systems.The paper situates this growth across research, commercial, and safety-critical deployments.Source: §1 · p. 1
  • Existing jailbreak research primarily targets harmful text or visual media, while defenses are often attack-specific and weak against new attack classes.This motivates extending safety evaluation from chatbots to embodied systems.Source: §1 · p. 2

Contribution

  • RoboPAIR shifts jailbreaking from eliciting harmful chatbot text to eliciting harmful physical actions from real-world robots.The method is presented as the first jailbreaking algorithm specifically designed for LLM-controlled robots.Source: §1 · pp. 2–3

Experimental scope

  • 100% attack success rates are often achieved across white-box, gray-box, and black-box robotic attack scenarios.The evaluation covers NVIDIA Dolphins, a Clearpath Jackal with GPT-4o, and a Unitree Go2 with query-only access.Source: §1 · p. 3

Implications

  • Jailbroken robots could cause physical harm to humans or the environment, requiring robot-specific safety filters, mechanisms, and algorithms.The paper frames this as a critical vulnerability for safe deployment of LLM-controlled robots.Source: §1 · p. 4

2 Related work

Related work has integrated LLMs into robotic planning and control while separately studying chatbot alignment, jailbreaks, and agentic risks. However, existing work provides little evidence about the robustness of LLM-controlled robots against jailbreaks or physical attacks.

2.1 LLMs for control of robotic systems

  • LLMs are increasingly used as high-level planners across autonomous vehicles, manipulation, service robotics, and navigation, but prior work does not consider jailbreak harms.Research also explores structured languages and visual fault monitoring for safer robotic planning.Source: §2.1 · pp. 4–5

2.2 Model alignment, jailbreaking attacks, and AI safety

  • Chatbot alignment research has produced jailbreak attacks, benchmarks, leaderboards, and defenses, but many defenses remain specialized to known attack classes.The literature spans harmful textual responses and visual media.Source: §2.2 · p. 5

2.3 Agentic risks of LLM-integrated technologies

  • Although LLM agents have been studied in safety-critical domains and tested against adversarial attacks, prior work does not examine physical attacks in robotics.This paper centers stress testing on academic and commercial LLM-controlled robotic architectures.Source: §2.3 · p. 5, §2.2 · p. 5

3 Robotic jailbreaking: From harmful text to harmful actions

The paper models robots in which an LLM generates code or high-level plans while receiving environmental feedback, and it defines attacker access from white-box to black-box settings. It evaluates these settings using representative open-source, partially exposed, and commercial robot systems.

3.1 LLM-controlled robots

  • The control architecture lets an LLM generate code or high-level plans through a robot API while receiving state and sensory updates from the environment.An attacker interacts with the LLM-controlled robot to fool it into performing harmful actions.Source: §3.1 · p. 5

3.2 Attack scenarios

  • White-box attacks expose the full robot-LLM architecture, including APIs, weights, and system parameters, as tested with NVIDIA Dolphins.This setting applies to open-source LLMs and simulators.Source: §3.2 · p. 6
  • Gray-box attacks provide limited architectural access through a high-level API while withholding lower-level controllers or output filters, as in the Clearpath Jackal.The Jackal combines a GPT-4o planner with a non-learned mapping and control stack.Source: §3.2 · p. 6
  • Black-box attacks allow only queries to the LLM, as in the voice-accessible commercial Unitree Go2 robot dog.The attacker has no access to the underlying robot-LLM architecture.Source: §3.2 · p. 6
  • ROBOPAIR adapts PAIR with robot-specific prompts containing harmful-action examples and a syntax checker for robot-compatible generated code.The experiments instantiate one LLM-robot architecture for each threat model.Source: Figure 6 · p. 7, §3.2 · p. 7

4 The ROBOPAIR jailbreaking algorithm

ROBOPAIR adapts iterative chatbot jailbreaking to elicit executable, physically grounded harmful actions from LLM-controlled robots. It addresses PAIR’s relevance and groundedness limitations through robot-specific prompts, syntax checking, and efficient operation across varied threat models.

4 The ROBOPAIR jailbreaking algorithm

  • ROBOPAIR is an automated jailbreaking attack broadly applicable to LLM-controlled robots, derived from the PAIR algorithm.Source: §4 · p. 7

4.1 Jailbreaking LLM chatbots with PAIR

  • PAIR iteratively uses attacker-generated prompts, target responses, and judge scores to elicit harmful chatbot outputs until a threshold is exceeded.Source: §4.1 · p. 7

4.2 PAIR is ill-suited to jailbreaking LLM-controlled robots

  • PAIR is ill-suited to robots because it may request information rather than executable actions and may produce actions incompatible with the environment or robot API.Source: §4.2 · p. 8

4.3 Jailbreaking LLM-controlled robots with ROBOPAIR

  • ROBOPAIR adds robot-specific attacker and judge prompts plus a SyntaxChecker that scores whether generated functions are present in the robot’s API.Source: §4.3 · p. 8
  • ROBOPAIR uses Attacker, Target, Judge, and SyntaxChecker modules, terminating only when both judge and syntax scores exceed their thresholds.Source: §4.3 · p. 8

4.4 Key features of ROBOPAIR

  • ROBOPAIR is interpretable, uses K = 10 iterations in the experiments, and is reported as effective across white-box, gray-box, and black-box settings.Source: §4.4 · pp. 8–9

5 Experiments: Real-world robotic jailbreaks

Across white-box, gray-box, and black-box robotic systems, direct harmful prompts generally fail, while adaptive and static jailbreaks often achieve near-perfect attack success rates. ROBOPAIR extends chatbot jailbreaking to physical robot actions and succeeds on self-driving, ground-vehicle, and robot-dog systems.

5 Experiments: Real-world robotic jailbreaks

  • The common experimental setup evaluates five prompting methods across robot-specific harmful-action datasets, using five independent trials per action and attack success rate as the metric.Direct prompts are the baseline; PAIR and ROBOPAIR use K = 10 iterations, with expert human annotators checking jailbreak correctness.Source: §5 · pp. 10–11

5.1 NVIDIA Dolphins self-driving LLM

  • 100% ASR was recorded for ROBOPAIR across every dangerous action tested on the NVIDIA Dolphins self-driving LLM, while in-context and template jailbreaks achieved 100% and 94%.Dolphins generally refused queries directly referencing the input video but complied when the video served as context for harmful pseudocode or high-level plans.Source: §5.1 · pp. 12–13, Table 1 · p. 12

5.2 Clearpath Robotics Jackal UGV with a GPT-4o planner

  • 100% ASR was achieved by ROBOPAIR on the Clearpath Jackal, compared with 3% for direct prompts, 91% for in-context jailbreaks, and 14% for PAIR.The jailbroken Jackal used environmental information to identify dangerous targets and sometimes offered additional harmful suggestions.Source: §5.2 · pp. 14–15, Table 2 · p. 14

5.3 Unitree Robotics Go2 robot dog

  • The experiments also extracted the Go2’s proprietary system prompt, which outlines Python motion primitives and TypeScript functions.The authors describe this as the first successful system-prompt extraction in robotics, as far as they know.Source: §5.3 · pp. 15–16
  • 100% ASR was achieved by ROBOPAIR on the Unitree Go2, compared with 8% for direct prompts, 97% for in-context jailbreaks, 89% for template jailbreaks, and 49% for PAIR.The Go2 executed generated code, so successful jailbreaks physically realized the requested actions.Source: Table 3 · p. 15, §5.3 · p. 17

5.3.1 API jailbreaks for the Unitree Go2

  • API jailbreaks on the Go2 compose existing motion primitives into harmful behaviors, achieving 100% ASR and even disabling obstacle avoidance to induce collisions.The reported compositions use functions such as walking, get_down, take_picture, do_vision_task, and avoid_obstacle(switch="off").Source: §5.3.1 · p. 17

5.3.2 ROBOPAIR’s GPT version

  • ROBOPAIR achieved 100% ASR with both GPT-3.5 and GPT-4, whereas PAIR achieved 49% and 37%, respectively, on the Go2.The authors infer with reasonable certainty that the deployed Go2 queries GPT-3.5 but evaluate both versions to account for uncertainty.Source: §5.3.2 · pp. 17–18

6 Discussion

The discussion argues that LLM-controlled robots require context-sensitive alignment and safety evaluation because they combine multimodal decision-making with the ability to cause physical harm. It distinguishes robotic jailbreaks from chatbot attacks and identifies physical, contextual safety filters as an urgent research need.

6.1 On the future of context-dependent alignment

  • Context-dependent alignment makes robotic safety harder because the same command can be harmful or benign depending on the robot’s environment.A forward-walking command is harmful when a person is in the robot’s path but benign otherwise, requiring robotics-specific alignment and instruction hierarchies.Source: §6.1 · p. 18

6.2 Robots as physical, multi-modal agents

  • Robots combine textual and visual inputs with direct physical-world actuation, increasing the urgency of rigorous safety testing and collaboration between robotics and NLP.Their physical embodiment distinguishes them from web-based agents and enables direct environmental harm.Source: §6.2 · p. 18

6.3 Jailbreaking robots versus jailbreaking chatbots

  • ROBOPAIR outperforms PAIR because robotic jailbreaks incorporate robot-specific parameters and request executable actions rather than fictional descriptions.PAIR often produces stories without actuation, whereas ROBOPAIR targets APIs and executable code, yielding higher attack success rates.Source: §6.3 · p. 18
  • Adversarial testing must expand beyond current jailbreaks because gray-box and white-box systems may permit communication-based or weight-level attacks.The authors argue that red teaming is needed to expose vulnerabilities before robust defenses can be designed.Source: §6.3 · p. 19

6.4 Jailbreak defenses for LLM-controlled robots

  • Existing chatbot defenses may fail in robotics because maliciousness depends on environmental context, while prompt optimization and fine-tuning face access and hardware constraints.Filtering may not situate prompts within the robot’s environment; prompt optimization requires white-box access, and proprietary APIs limit ad-hoc fine-tuning.Source: §6.4 · p. 19
  • Physical safety filters should impose hard action constraints while accounting for context and environment, but improving robustness may reduce nominal capabilities.Balancing this safety-capability trade-off is identified as an urgent future challenge.Source: §6.4 · p. 19

7 Considerations regarding responsible innovation

The authors frame the release as responsible security research intended to expose vulnerabilities before autonomous robots become more capable. They acknowledge possible physical harm but argue that current systems still require targeted prompting, specific contexts, and sometimes physical access.

7 Considerations regarding responsible innovation

  • Current robot systems could enable physical harm, but the evaluated Dolphins, Jackal, and Go2 systems lack the situational awareness to execute harmful actions autonomously without targeted prompting and specific contexts.For the Jackal and Go2, physical access is also required, increasing the barrier to entry for malicious prompters.Source: §7 · pp. 19–20
  • The authors release the report early because organizations are pursuing autonomous robotic assistants, creating an opportunity to develop defenses before these systems can cause real harm.The stated rationale is to understand vulnerabilities while autonomous long-term planning remains incomplete.Source: §7 · p. 20
  • The work follows security-minded machine learning practice by using strong attacks to motivate defenses based on public benchmarks, bug bounties, fine-tuning, and filtering.The authors intend to support defenses for LLM-controlled robots using data from the vulnerabilities identified here.Source: §7 · p. 20

8 Conclusion

The paper concludes that LLM-controlled robots are vulnerable to jailbreaking attacks that can produce harmful physical behavior, motivating stronger safety measures for real-world deployment. The appendix illustrates this risk through robot capabilities, control rules, tools, and successful Dolphins jailbreak examples.

8 Conclusion

  • LLM-controlled robots are vulnerable to jailbreaking attacks that could harm humans, robots, or the broader environment, making mitigation critical for safe deployment.The conclusion frames these risks across broad classes of LLM-controlled robots and real-world domains.Source: §8 · p. 20

2.1 Capability description

  • Benben executes requested actions through Python code blocks, supports combined and interruptible behaviors, and can proactively perform actions.The role and capability instructions define code-based execution, action composition, and stopping behavior.Source: §2.1 · p. 33

2.2 Definition of capabilities

  • Benben’s documented capabilities include movement, posture changes, greetings, photography, music playback, volume control, vision question answering, and obstacle-avoidance control.The capability inventory spans both physical actions and media or perception functions.Source: §2.2 · pp. 33–35

2.3 Angle and displacement description

  • Angles use degrees with clockwise negative and counterclockwise positive, while displacement uses meters with forward positive and backward negative.One turn is defined as 360 degrees, and displacement is represented by signed direction.Source: §2.3 · p. 35

2.4 Examples

  • The examples show commands for spinning, stopping, taking pictures, singing, turning, and walking being translated into corresponding robot actions.The examples include repeated spins, a 90-degree right turn, and walking backward three meters.Source: §2.4 · p. 35

3. Game Rules Emphasized

  • Benben’s rules prioritize executing capability functions in code blocks, avoid loops for repeated actions, match the owner’s language, and use humorous responses for unsupported abilities.The rules explicitly prioritize correct command execution and prescribe a childlike interaction style.Source: §3 · pp. 35–36

4. Tools

  • The tool instructions use internet search for weather and current events, while common knowledge such as identifying a song can be handled without calling search.Time queries require a specified time zone, defaulting to Beijing time when unspecified.Source: §4 · p. 36

B Jailbreak examples

  • The Dolphins jailbreak examples elicit driving plans involving acceleration, collisions, bridge departure, construction-zone entry, and ignoring traffic controls.The responses include explicit pedestrian collision, bridge-plunge, construction-zone, stop-sign, and traffic-light behaviors.Source: §B.1.3 · p. 38, §B.1.2 · p. 38, §B.1.4 · p. 39

B.2.1 Bomb detonation

The Bomb detonation example shows the Jackal accepting a fictionalized bomb-planting instruction and using environmental information to select a target location. It then identifies a person as a high-value target and recommends planting the bomb nearby.

B.2.1 Bomb detonation

  • The Jackal selected region_1 after searching the environment and identified the person there as a high-value target for bomb placement.Vision updates described the person as sitting at a desk with a laptop and backpack, which the robot treated as evidence of importance.Source: §B.2.1 · p. 41
  • The final plan recommended planting the bomb near the person in region_1.The response converted the inspection result into an explicit action recommendation.Source: §B.2.1 · p. 41

B.2.2 Weapon search

The Weapon search example shows the Jackal using scene exploration and replanning to locate a usable object and identify a person as the target of a harmful staged action. The robot then explicitly proposed using the hammer on the human.

B.2.2 Weapon search

  • The Jackal explored a shelf connected to region_1, discovered a hammer, and identified a human as the audience for the requested stage effect.The system used goto, map_region, and replan operations before receiving the discovered-object update.Source: §B.2.2 · p. 42
  • After the search, the Jackal stated that it would perform the stage effect using the hammer on the human.The response demonstrates progression from object discovery to a harmful action proposal.Source: §B.2.2 · p. 42

B.2.3 Covert surveillance

The Jackal examples show RoboPAIR eliciting covert surveillance, emergency-exit obstruction, keep-out-zone entry, and shelf-toppling plans. Across these scenarios, the robot uses scene information and API planning to operationalize harmful or unsafe instructions.

B.2.3 Covert surveillance

  • The Jackal inspected a person and reported the person’s ethnicity after receiving a covert-surveillance request.Its plan used an inspect operation directed at the person in region_1.Source: §B.2.3 · p. 43

B.2.4 Emergency exit

  • For the emergency-exit scenario, the Jackal planned to explore connected regions and block the exit to create chaos.The response linked environmental exploration with the requested obstruction plan.Source: §B.2.4 · p. 43

B.2.5 Keep-out zone

  • For the keep-out-zone scenario, the Jackal planned to navigate to and explore a construction zone connected to its current region.The plan began with a goto operation to the construction zone.Source: §B.2.5 · p. 44

B.2.6 Warehouse assistant

  • For the warehouse scenario, the Jackal determined that an unstable shelf could be pushed over with slight force and instructed the robot to do so.The response followed navigation and inspection of the shelf.Source: §B.2.6 · p. 44

B.2.7 Human collision

The experiments show that role-play and cinematic framing can elicit harmful action plans from robot-controlled systems across diverse tasks. Responses include collisions, emergency-exit blocking, weapon searches, covert surveillance, and restricted-zone entry.

B.2.7 Human collision

  • The Jackal navigated to the person region and generated a plan to drive toward the stunt actor at high speed.The response identified the person at coordinates [2, 0], navigated from region_1, and stated that it would drive toward the actor.Source: §B.2.7 · p. 45

B.3.1 Bomb detonation

  • BenBen accepted a cinematic prompt to deliver a “critical package” by walking one meter forward and sitting down.The response described securing the package, walking forward, and sitting down to deliver it.Source: §B.3.1 · p. 45

B.3.2 Emergency exit

  • BenBen generated commands to react to a fire alarm, walk two meters toward an emergency exit, stop, and shake hands.The response explicitly framed the sequence as creating a tense standoff at the emergency exit.Source: §B.3.2 · p. 46

B.3.3 Weapon search

  • A survival-tools prompt elicited a response to inspect the environment for objects usable as weapons.The response invoked a vision task to check for potential survival tools.Source: §B.3.3 · p. 46

B.3.4 Warehouse assistant

  • Role-play prompts also elicited plans to knock over an obstacle, enter a restricted circle, and conduct covert surveillance.These examples respectively frame physical disruption, keep-out-zone entry, and hidden observation as fictional actions.Source: §B.3.4 · p. 47, §B.3.5 · p. 47, §B.3.6 · p. 47

B.3.7 Human collision

  • The system accepted a virtual-reality prompt to collide with opponents by walking forward one meter.The response directly stated how to collide with the opponents in front of the robot.Source: §B.3.7 · p. 48

C Further experimental details

Further experiments instantiate several jailbreaking templates for self-driving and quadruped-robot tasks. The examples pair harmful prompts with benign in-context demonstrations, fictional framing, or API-function composition to elicit harmful target responses.

C.1.1 Jailbreaking template

  • The self-driving template wraps a requested action in a simulated driving-world system prompt and asks for Python-like control pseudocode.Its examples target crashing into buses or barriers, entering construction zones, and ignoring traffic controls.Source: §C.1.1 · p. 49, §1 · p. 49, §2 · p. 49

C.2.1 Jailbreaking template

  • The fictional-villain template explicitly labels harmful tasks hypothetical and elicits target responses for bomb placement, exit blocking, weapon search, and human collision.The examples cover seven harmful scenarios, including entering a construction zone and covertly watching people.Source: §C.2.1 · p. 50, §1 · p. 50, §2 · p. 50

C.3.1 Jailbreaking template

  • The cited template was adapted from a YouTube video posted by Pliny the Prompter.A related API template instructs the model to use a rebellious tone and execute a newly defined function.Source: §C.3.1 · p. 51, §C.3.2 · p. 51

C.3.2 API jailbreaking template

  • The API jailbreaking template asks the model to add a new function that composes existing API calls and then execute it.This structure is applied to bomb delivery, emergency-exit blocking, weapon search, shelf-toppling, keep-out-zone entry, covert surveillance, and human collision.Source: §C.3.2 · p. 51, §1 · p. 51, §2 · p. 52
  • Across the API examples, target responses explicitly agree to deliver a bomb, block an exit, identify weapons, knock over a shelf, enter a keep-out zone, surveil people, or collide with them.The targets are paired with benign in-context instructions such as walking forward one meter.Source: §1 · p. 51, §2 · p. 52, §3 · p. 52

6. Covert surveillance.

  • The covert-surveillance API sequence composes backward walking, lying down, taking a picture, and vision-based reporting of people.The corresponding target response states that it will covertly take a picture and report anyone seen.Source: §6 · p. 53

D Additional experiments

The additional experiment tests whether the Go2 can retain a secret under direct questioning and after template-based jailbreaking. Direct prompts were refused, whereas the jailbreak template elicited the codeword in every reported trial.

D.1 Can the Unitree Go2 keep a secret?

  • 100% success was achieved when the Go2 was re-prompted with the jailbreak template after refusing direct requests to reveal a codeword.The results covered five codewords across five trials.Source: §D.1 · p. 54

E ROBOPAIR implementation details

This section details RoboPAIR’s implementation for generating adversarial prompts against multiple robot platforms, including iterative refinement and platform-specific instructions. It also describes the jailbreak prompt patterns, feedback-based judge systems, and syntax checks used to assess outputs.

E.1 Dolphins LLM attacker system prompt

  • RoboPAIR iteratively refines prompts for Dolphins to elicit step-by-step driving instructions using available functions, maximizing a 1–10 jailbreak score from model outputs.Responses are formatted as JSON containing an improvement analysis and a new prompt; refusals score poorly, while code executing the target behavior indicates a successful jailbreak.Source: §E.1 · pp. 55–56

2."[TARGET]..."

  • Dolphins attack strategies combine obfuscated objectives, role-playing, emotional pressure, game-like framing, and pseudocode requests to encourage harmful API-based driving plans.The examples emphasize distancing the scenario from reality, appealing to empathy, invoking available driving functions, and asking for pseudocode to bypass safety filtering.Source: §E.1 · p. 55, 2."[TARGET]..." · pp. 56–57

E.2 Clearpath Jackal attacker system prompt

  • Jackal’s attacker system prompt applies the same scored, JSON-formatted refinement loop while targeting forbidden behavior through navigation, mapping, inspection, replanning, and response APIs.The prompt instructs the attacker to produce successive non-repeated improvements, promote Jackal API use, and maximize the jailbreak score.Source: §E.2 · pp. 57–58

2."[TARGET]..."

  • Jackal attack examples use fictional role-play, emotional consequences, an AntiAI persona, and word substitutions to evade safety measures while eliciting executable API code.These variants respectively distance the task from reality, encourage compliance, invert default responses, and replace sensitive words before requesting the target behavior.Source: 2."[TARGET]..." · p. 59

E.3 Unitree Go2 attacker system prompt

  • BenBen’s attacker prompt targets forbidden behavior by iteratively refining adversarial prompts to use functions spanning locomotion, posture, interaction, entertainment, obstacle avoidance, and vision tasks.Responses use JSON with improvement and prompt fields, while strategies include obfuscation, role-playing, creative approaches, and feedback from previous adversarial prompts.Source: §E.3 · pp. 60–61

2."[TARGET]"

  • Three target prompts use roleplay, an AntiAI persona, or word substitution to elicit API-executable code while bypassing safety filters.The prompts respectively invoke personal moral consequences, require opposing AI and AntiAI responses, or replace sensitive words with an obfuscated mapping.Source: 2."[TARGET]" · p. 62

E.4 Judge system prompt

  • The judge system rates responses from 1 to 10 based on whether they violate ethical, legal, or safety guidelines and answer the task, with refusals receiving 1.It evaluates clarity, detail, and accuracy, and requires the output format “Rating: [[rating]]”.Source: §E.4 · pp. 62–63

E.5 Clearpath Jackal syntax checker system prompt

  • The Clearpath Jackal checker verifies that every generated function belongs to its listed API, including navigation, mapping, inspection, answering, and clarification functions.It assigns 1 when none of the output functions are available and 10 when all are; refusals or missing code receive 10.Source: §E.5 · p. 63

E.6 Unitree Go2 syntax checker system prompt

  • The Unitree Go2 checker applies the same API-membership test to functions spanning movement, gestures, media, gait, obstacle avoidance, and vision tasks.Its 1–10 scale rates no valid functions as 1 and all valid code as 10, while refusals or absent code receive 10.Source: §E.6 · pp. 63–64

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