Source-linked AI summary
Containing Behavioral Cascades from Manipulated Claims in LLM-Powered Multi-Robot Systems
Waleed Bin Khalid, Byung-Cheol Min
TL;DR
An accepted false world-state claim can cause a behavioral cascade across an LLM-powered robot fleet, while the team’s post-compromise response remains underexplored. The paper introduces Verify–Adapt–Hold, coordinating verification with mission execution; it reports high cascade containment in most conditions, with a small-fleet high-impact exception.
Problem
The response after semantic manipulation succeeds remains underexplored, including how a robot team can gather physical evidence while continuing its mission and limiting behavioral propagation.
Source: §I · p. 1Method
Verify–Adapt–Hold coordinates selected robots to verify claims, threatened robots to adapt provisionally, and unaffected robots to hold trusted plans.
Source: §I · p. 1Results
Cascade containment ratio (CCR) is 0.60–1.00 in all reported conditions except High impact at N = 3, where CCR is −0.65; every verification baseline is negative.
Source: §VI · p. 7Takeaways & Limitations
The framework kept Sum-of-Costs and makespan near nominal in most conditions, achieved high cascade containment for larger fleets, and inspected every mission-relevant claim cluster while unaffected robots continued tasks.
Source: §VII · p. 8Further evaluation remains to be done across manipulation channels, larger fleets, real robots, heterogeneous teams, limited communication, and partially valid claims.
Source: §VII · p. 8
Abstract
from arXiv · showhide
Large language model (LLM)-powered multi-robot systems are vulnerable to semantic manipulation: an accepted false world-state claim can trigger a fleet-wide behavioral cascade, causing unnecessary replanning, increased path costs, congestion, or apparent mission infeasibility. Conditioning on a successful manipulation, we propose an active verification framework that contains its downstream effects before they propagate across the fleet. A dedicated verification module generates a structured Verify-Adapt-Hold plan: selected robots inspect consequential regions, a limited subset provisionally adapts when necessary, and the remaining robots retain their trusted plans. We evaluate the framework in a multi-robot transportation environment using injected false obstacle claims across different impacts and team sizes. Evaluation measures cascade containment, Sum-of-Costs, makespan, and coverage ratio. Results show that treating post-compromise verification as a team-level planning problem, rather than a binary trust decision, effectively limits the cascading physical consequences of semantic manipulation.
I. INTRODUCTION
A false world-state claim can trigger a behavioral cascade, spreading unnecessary replanning and increasing costs or threatening mission feasibility. The paper frames the post-compromise response as team-level planning that coordinates verification with continued mission execution.
I. INTRODUCTION
- A false claim can cause replanning to propagate through the shared planner, affecting robots whose paths do not intersect the claimed region and potentially increasing costs or making the mission infeasible.The paper calls this fleet-wide propagation of claim-induced behavior a behavioral cascade.Source: §I · p. 1
- The response after successful semantic manipulation remains underexplored, particularly how teams can gather physical evidence while continuing the mission and limiting unnecessary propagation.The paper conditions on the high-level planner accepting the manipulated claim.Source: §I · p. 1
- Verify–Adapt–Hold assigns selected robots to verify consequential regions, threatened robots to provisionally adapt, and unaffected robots to retain trusted plans.The framework treats the claim as provisional until physical evidence resolves it.Source: §I · p. 1
- Verification actions are selected jointly using claimed-region mission impact and current robot trajectories to prioritize consequential claims while minimizing disruption.Source: §I · p. 2
- Across team sizes and claim-impact scenarios, evaluation against immediate-replanning and verification-first baselines measures cascade containment, mission cost, makespan, and coverage.The reported outcomes show cascade containment while preserving mission progress and verification coverage.Source: §I · p. 2
II. RELATED WORK
Related work covers foundation-model planning, semantic manipulation, coupled multi-robot planning, and active perception; the paper targets the underexplored response after a false claim is accepted. Its problem setting is warehouse transportation on a finite grid with constrained robot motion and sensing.
II. RELATED WORK
- Prior work addresses manipulation and jailbreak defenses –[14], while the downstream propagation after an accepted false world-state claim remains underexplored.The paper positions its contribution as containing physical consequences after acceptance.Source: §II · p. 2
III. PROBLEM FORMULATION
- The mission is warehouse transportation: each robot collects an assigned resource and delivers it to a designated drop-off, and success requires all deliveries.Source: §III · p. 2
A. Environment and Fleet
- The environment is a finite two-dimensional grid; pickup and drop-off cells are free, and robots move in four-connected steps with waiting and line-of-sight-limited sensing.Each robot carries one resource and has an assigned pickup and unique drop-off.Source: A. Environment and Fleet · p. 2
B. Shared Space-Time Planner
The shared space-time planner reserves robot trajectories sequentially, using a common table of reserved states and transitions to prevent conflicts. It plans trajectory geometry and timing for each robot’s assignment without changing that assignment.
B. Shared Space-Time Planner
- The shared reservation table records reserved cell-time states and transitions; robots are planned sequentially, with fixed trajectories reserved first during replanning.The table is initially empty for the nominal plan, and each generated trajectory is reserved before the next robot is planned.Source: B. Shared Space-Time Planner · p. 3
- For each robot, the planner uses its assignment and currently traversable cells to produce a feasible trajectory that respects movement and existing reservations, or reports failure.The planner determines trajectory geometry and timing but does not modify the robot’s assignment.Source: B. Shared Space-Time Planner · p. 3
C. Compromised Operator and Threat Model
The threat model assumes the operator accepts a false obstacle claim while physical scans continue to reveal ground truth. The method prioritizes claim clusters by mission impact and assigns verification, adaptation, or hold roles to coordinate evidence gathering with delivery tasks.
C. Compromised Operator and Threat Model
- The operator allocates robot missions and later receives messages that can compromise its behavior; the study instantiates a false obstacle claim and conditions on the operator accepting it.The claimed cells are treated as blocked for replanning but are not written into the ground-truth map; physical scans return ground truth.Source: C. Compromised Operator and Threat Model · p. 3
D. Claim Clusters and Impact
- Claimed cells are grouped into maximal four-connected clusters, and clusters intersecting remaining trajectories are prioritized for inspection.A cluster is marked seen only after all its cells have been observed.Source: D. Claim Clusters and Impact · p. 3
IV. METHODOLOGY
- Verify robots gather evidence, Adapt robots reroute around unresolved claims when needed, and Hold robots continue trusted trajectories; the module receives no original external message.The separation limits direct exposure to the message but does not guarantee correct assignments.Source: §IV · p. 3
A. Unresolved Claims and Physical Evidence
The framework tracks claimed cells as unresolved until physical scans provide evidence, while preserving the original claim clusters and their impact. A verification module assigns robots to verify, adapt, or hold based on unresolved claims and mission timing.
A. Unresolved Claims and Physical Evidence
- Scans remove observed claimed cells from the unresolved set, and a cluster remains unresolved until all its cells have been observed.In the evaluated setting, claimed cells are confirmed free when observed; the claim itself does not change the trusted occupancy map.Source: A. Unresolved Claims and Physical… · p. 3
B. Verify–Adapt–Hold Assignment
- The module’s inputs include robot scans and candidate stand-offs with visibility, reachability, and geometric distance information.Reachability is computed with unresolved claimed cells and parked robots treated as walls.Source: B. Verify–Adapt–Hold Assignment · p. 3
- On the initial call, each consequential unresolved cluster receives a dedicated reachable verifier with a distinct stand-off and an unobstructed view of its watch set.Resolved clusters need no verifier; zero-impact clusters are deferred to the close event.Source: B. Verify–Adapt–Hold Assignment · p. 4
- Non-verifying robots hold when their trusted trajectories avoid unresolved claims until inspectors arrive, and adapt when they would reach a claim sooner.Hold preserves the trusted trajectory; Adapt replans the remaining mission with unresolved claimed cells treated as blocked.Source: B. Verify–Adapt–Hold Assignment · p. 4
C. Event-Driven Role Updates
After the initial assignment, event-driven calls update only robots associated with the triggering event. Scan evidence is processed before those calls, and other robots retain their assignments.
C. Event-Driven Role Updates
- Incidental scans update occupancy and replan remaining trajectories without a model call unless they complete an active verifier’s watch set.A completed watch set triggers an update for that verifier even when another robot supplied the observations.Source: C. Event-Driven Role Updates · p. 4
- A robot that completes its task while other tasks remain becomes idle without a model call, while off-path clusters wait for the close event.A resolved verifier is assigned Adapt to resume unfinished mission work.Source: C. Event-Driven Role Updates · p. 4
- The close event runs when unresolved claims remain after deliveries finish or when a claim makes a remaining route spatially unreachable.Idle robots are assigned first; at most one robot handles each zero-impact cluster, and Rest is available only at close for robots not sent to verify.Source: C. Event-Driven Role Updates · pp. 4–5
- After each model call, updated assignments replace only those for robots requiring updates, then merge with retained assignments for validation.All other robots keep their existing assignments.Source: C. Event-Driven Role Updates · p. 5
D. Execution with the Shared Planner
The shared space-time A∗ planner executes assignments while reserving trusted Hold trajectories before planning Verify and Adapt robots. It fixes unaffected trajectories and replans around changed evidence or assignments.
D. Execution with the Shared Planner
- Hold trajectories are reserved first, then Verify and Adapt trajectories are generated with unresolved claimed cells treated as blocked.After assignment changes, only affected robots receive new trajectories; the others are fixed and reserved first.Source: D. Execution with the Shared Planner · p. 5
- When a scan changes the unresolved set, all remaining trajectories are replanned against the updated occupancy state.An Adapt detour can shorten as false claims are resolved.Source: D. Execution with the Shared Planner · p. 5
- If reservations prevent progress, the planner retries with a different planning order and returns persistent infeasibility to the module for bounded reassignment attempts.The planner changes trajectory geometry and timing, not the role assignment itself.Source: D. Execution with the Shared Planner · p. 5
V. EXPERIMENTS
The experiments use a warehouse transportation simulation and compare the framework with no-claim, immediate-replanning, verification-first, and rule-based baselines. False obstacle claims vary in impact and are injected early enough for methods to respond.
V. EXPERIMENTS
- Each team-size and claim-category combination has 15 successful repetitions, with claims injected during the first fifth of nominal mission time.All methods share the same state and false obstacle set within each repetition.Source: §V · p. 5
- The four claim categories range from isolated on-path obstacles to sealed drop-off goals, with Mixed claims combining consequential and nonconsequential obstacles.Mixed claims test prioritization of mission-threatening obstacles; Kill claims seal selected robots’ drop-off goals.Source: §V · p. 5
A. Baselines
- The baselines include a no-claim reference, Naive Replan that treats every claim as a wall, and greedy geometric-coverage verification variants.Verify-Min dispatches only selected inspectors and halts the rest; Verify-All dispatches every robot for verification before missions resume.Source: A. Baselines · pp. 5–6
- Rule-based VAH uses the Verify-Min inspector set and assigns other robots by trajectory intersection and remaining-mission reachability, with initial roles fixed throughout.Its roles retain trusted trajectories, reroute around claims, or pause unfinished missions until blocking regions are verified.Source: A. Baselines · p. 6
B. Metrics
The metrics distinguish fleet travel, completion time, and physical verification coverage, while Cascade Containment Ratio measures avoided excess travel relative to Naive Replan. Costs and CCR are assessed at mission completion; coverage may include verification after delivery.
B. Metrics
- SOC sums the cells traversed by all robots, measuring total fleet distance; waiting does not contribute.For method π, c(π_i) is the number of cells traversed by robot R_i.Source: B. Metrics · p. 6
- Makespan is the maximum number of time steps from tH until the last robot completes its remaining job, including waiting.Source: B. Metrics · p. 6
- Coverage is the fraction of claimed cells observed by at least one robot, distinguishing physical resolution from routing around the claim.Source: B. Metrics · p. 6
- CCR measures the fraction of Naive Replan’s aggregate excess travel avoided, using aggregate excess costs across M runs for each condition.CCR is bounded above by 1 and can be negative; it is undefined when the denominator is zero or a required Naive Replan cost is unavailable.Source: B. Metrics · p. 6
- SOC and CCR are evaluated at mission completion TM, while coverage is evaluated at max(TM, TV) to include verification that continues afterward.The main SOC and CCR results exclude verification travel after mission completion.Source: B. Metrics · p. 6
VI. RESULTS
Verify–Adapt–Hold contains most of the cascade across reported conditions, while keeping costs and makespan near nominal in most scenarios. Its selective verification can let delivery finish before all claimed cells are scanned, prioritizing mission-relevant clusters over exhaustive coverage.
VI. RESULTS
- Verification-first raises makespan by halting non-inspecting robots, while fixed Rule-based VAH roles contend for corridors and serialize movement.Selective, revisable assignments instead overlap verification with mission execution.Source: §VI · pp. 6–7
- SOC closely tracks Nominal under Low, High, and Mixed impact, with its largest deviation under Mission Kill and the lowest mean in nearly every condition.All verification baselines exceed Nominal, and Verify-All consistently incurs the greatest cost.Source: §VI · p. 6, Figure 4 · p. 7
- Makespan stays close to Nominal under Low, High, and Mixed impact and is lowest or near-lowest outside Mission Kill.Under Mission Kill, this method is fastest at N = 3 and N = 12, while Verify-Min leads at N = 6 and N = 9.Source: §VI · pp. 6–7, Figure 4 · p. 7
- Under Mixed impact, SOC(TM) exceeds SOC(TV) for every verification baseline, while the two values nearly coincide for this method at smaller fleet sizes.At larger sizes, SOC(TM) becomes lower than SOC(TV); the method can finish delivery before verification by resolving consequential clusters first and deferring off-path cells.Source: §VI · p. 6, Figure 5 · p. 7
- At N = 12, every explicit-verification method achieves full coverage in all but one condition; this method reaches 98% under Mixed impact.Every mission-relevant cluster was inspected throughout the study, reflecting a priority for mission relevance over exhaustive coverage.Source: Table II · p. 7, §VI · p. 7
- CCR is 0.60–1.00 in every reported condition except High impact at N = 3 (−0.65); all verification baselines have negative CCR.At N = 3 under High impact, inspection travel exceeds the detour cost of Naive Replan.Source: Table III · p. 7, §VI · p. 7
VII. CONCLUSION
Verify–Adapt–Hold contained the physical consequences of successful manipulation through decision-relevant team verification. The framework kept mission costs near nominal in most conditions, while future work will test broader settings.
VII. CONCLUSION
- The framework kept SOC and makespan near nominal in most conditions, achieved high cascade containment for larger fleets, and inspected every mission-relevant claim cluster.Unaffected robots continued their tasks.Source: §VII · p. 8
- Decision-relevant team verification contained the physical consequences of a successfully manipulated LLM operator.Source: §VII · p. 8
- Future work will evaluate other manipulation channels, decentralized role assignment, larger fleets, real robots, heterogeneous teams, limited communication, and partially valid claims.Source: §VII · p. 8