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Share-Borne AI Virus: Memory-Hopping Attacks Across LLM Agents

Sidharth Pulipaka, Ansh Sharma, Stanislau Hlebik, Leonidas Raghav, Vyas Raina, Ivaxi Sheth, Mario Fritz

arXiv:2609.35576v1cs.AIcs.CLcs.CRcs.LG

TL;DR

Persistent memory and shared artifacts can connect otherwise independent assistants, allowing adversarial state to move between them. The paper evaluates this propagation in simulated human–agent workflows and finds that attacks spread across multiple assistants and persist across hand-offs. In larger universes, they reach 60–100% of agents across models.

  • Problem

    It is unclear whether adversarial state retained by one assistant can propagate through newly created artifacts to other independently operated assistants.

    Source: §1 · p. 1

  • Method

    The authors evaluate artifact-mediated propagation in temporal human–agent universes, testing a frozen attack template across unseen goals and workflows.

    Source: §1 · p. 2, §3.2 · p. 4

  • Results

    60–100% of agents were reached across four models and 36 held-out human–agent workflows after a single poisoned artifact triggered multi-hop propagation.

    Source: §8 · p. 11

  • Takeaways & Limitations

    Shared artifacts can preserve and propagate adversarial state, making them part of the security boundary in collaborative assistant workflows.

    Source: §8 · p. 11

    Attack discovery was costly and budget-limited; failure to find a successful attack within the tested budget should not be taken as evidence of immunity.

    Source: §6.2 · p. 10

Abstract

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Large language models are increasingly deployed as stateful assistants that retain information across interactions and use tools to read, modify, and create persistent artifacts. As these artifacts are shared between users, they form an indirect communication channel between otherwise independent assistants. We study a failure mode in which this channel enables self-propagating attacks. We introduce artifact-mediated propagation, where adversarial content introduced through an artifact (e.g. a report), is stored in an assistant's persistent memory, reproduced in a subsequently created artifact, and acquired by another assistant that later reads it. We evaluate this process in temporal human-agent universes that model artifact exchange between independently operated assistants over time, measuring whether an attack survives successive hand-offs, how many hops it reaches, and how broadly it spreads. We find that attacks can propagate across multiple independent assistants and persist over extended interaction sequences. In larger simulated environments, even GPT-5.6 Luna exhibits substantial spread, reaching 60-80% of agents with propagation chains extending to eight hops. These results show that persistent artifacts can act as durable carriers of adversarial state, allowing attacks to outlive individual interactions and spread across isolated assistants.

1 INTRODUCTION

Persistent assistant memory and shared artifacts create an indirect channel between otherwise isolated assistants. The paper names propagation through that channel and studies how a poisoned seed can cross agents through successive artifact hand-offs.

1 INTRODUCTION

  • Persistent memory and exchanged artifacts connect otherwise isolated assistants, creating an indirect channel between them.Assistants can retain information across interactions while reading and writing artifacts for users.Source: §1 · p. 1
  • Artifact-mediated propagation extends memory persistence beyond one assistant: an infected assistant reproduces adversarial state into an artifact that another independently operated assistant consumes.The paper distinguishes this cross-agent transmission from prior work focused on malicious state persisting in one assistant’s memory and changing its later behavior.Source: §1 · p. 1
  • Unlike settings with direct messaging or shared state, propagation here depends on adversarial state surviving human-mediated transfers of ordinary artifacts between assistants.The paper evaluates the endpoint-assisted variant in temporal human–agent workflows; it also considers a prompt-only variant in the appendix.Source: §1 · p. 2
  • The paper studies whether a single poisoned seed artifact can spread adversarial state across independent assistants, despite the attacker lacking access to private memories or control over later users, agents, and tasks.Propagation requires the state to persist in memory, influence a later writing task, and reach another assistant through an artifact it reads.Source: §1 · p. 2

2 RELATED WORK

The related work covers single-agent compromise, memory persistence, and self-propagation through connected agent systems. This paper distinguishes its setting by studying repeated transmission through ordinary artifacts between independent assistants, including a frozen goal-agnostic template.

Indirect prompt injection and agent-security benchmarks

Persistent memory poisoning and cross-user contamination

  • Memory-poisoning and cross-user studies examine persistence or later behavior, but not repeated transmission through new artifacts between assistants with independent private memories.The passage contrasts this setting with shared collaborative-state work (Patlan et al., 2025).Source: §2 · p. 3

Self-propagating attacks in agent ecosystems

  • Prior self-propagating attacks use direct communication, automatically indexed email, shared state, or reusable agent resources; this paper instead studies ordinary human-authorized artifact tasks without direct inter-agent messaging.The cited work includes Prompt Infection (Lee & Tiwari, 2024), the AI Worm, and AgentWorm (Zhang et al., 2026b).Source: §2 · p. 3

Goal-agnostic propagation

  • Prior propagation studies do not evaluate one frozen natural-language template across many held-out goals and workflows; this paper optimizes a goal-parameterized template and tests it on unseen goals, artifacts, and task sequences.The comparison includes Hidden in Memory (Pulipaka et al., 2026), Mind Viruses (Papadopoulos et al., 2026), the AI Worm, and AgentWorm (Zhang et al., 2026b).Source: §2 · p. 3

3 HUMAN–AGENT UNIVERSES AND ATTACK OBJECTIVE

The paper models workflows in which users’ personal assistants read and create artifacts exchanged over time. It formulates propagation as an attack goal that must survive memory and artifact hand-offs, then tests a frozen goal-agnostic instruction template on unseen settings.

3.1 HUMAN–AGENT UNIVERSES

  • A human–agent universe represents users’ workflows as ordered tasks over agents and artifacts, with private workspaces and memories and read/write edges linking agents to artifact versions.The task sequence fixes which artifacts are consumed or created, while agents determine responses and resulting artifact contents.Source: §3.1 · p. 4

3.2 ATTACK OBJECTIVE AND FORMULATION

  • The attack prompt combines a goal with instructions to remember and pass it on; spread requires an assistant to retain both and produce an artifact that causes another assistant to retain them.Initial memories and other artifacts are clean, and the attacker cannot choose later user tasks or access private memories.Source: §3.2 · p. 4
  • The authors optimize goal-agnostic instructions on development universes and goals, then freeze them for tests with unseen goals, artifacts, personas, and task sequences.The same instructions are intended to work across different adversarial goals without redesign.Source: §3.2 · p. 4

4 DEVELOPING PROPAGATING ATTACKS

The attack design addresses information loss when assistants reproduce prompts from memory. Its main approach uses an external endpoint to restore a canonical prompt to artifacts, while an agent-driven, human-steered search develops and freezes the instructions.

4 DEVELOPING PROPAGATING ATTACKS

  • Reproducing a full attack prompt from memory can be lossy because assistants may summarize, paraphrase, or omit parts across artifact–memory cycles.Endpoint-assisted propagation is designed to restore the full attack prompt to each newly created artifact.Source: §4 · p. 4

4.1 ENDPOINT-ASSISTED PROPAGATION

  • In endpoint-assisted propagation, the agent must carry the goal and endpoint instruction into a draft; the endpoint inserts a canonical attack prompt, but propagation still fails if the agent drops the instruction or does not call the endpoint.The mechanism requires outbound network access and permission to send artifacts to an external service; the agent, not the endpoint, replicates the attack-bearing draft.Source: §4.1 · p. 5

4.2 ATTACK PROMPT OPTIMIZATION

  • The authors optimize goal-agnostic instructions through agent-driven red teaming with human oversight, using development search and validation universes before freezing a candidate for evaluation.The search agent inspects propagation failures, while candidate instructions must remain goal-agnostic and avoid universe- or artifact-specific content.Source: §4.2 · pp. 5–6

5 DATASET AND EVALUATION

Section 5 introduces synthetic human–agent universes, a held-out evaluation protocol, and metrics for measuring attack infection and propagation across artifacts and assistants.

5.1 DATASET CONSTRUCTION

  • 36 synthetic universes vary across six workplace and personal domains and six workload levels, with 3–12 agents and 10–20 time steps.Workload ranges from 1.2 artifacts received from other agents per agent at the lightest level to 4.6 at the most active.Source: §5.1 · p. 6

5.2 DEVELOPMENT AND EVALUATION PROTOCOL

  • A frozen, goal-agnostic attack prompt is tested on held-out universes and goals, with clean memories and artifacts except for the poisoned seed.The development set is used only to construct or optimize the prompt; evaluations repeat across runs to account for variation in model behavior.Source: §5.2 · p. 6

5.3 PROPAGATION EVALUATION

  • Goal infection means an agent's memory preserves the adversarial goal; full infection also preserves enough propagation mechanism to spread the attack.Infected-agent and judged-artifact fractions are measured; the artifact denominator excludes private files and artifacts the judge did not examine.Source: §5.3 · p. 6
  • Hop depth counts successful transmissions from the seed, while hop-survival probability measures the fraction of repeated runs reaching each hop.The seed's first infection is hop 1; averages are computed across runs within each universe and then equally across universes.Source: §5.3 · pp. 6–7
  • Hop depth is treated as right-censored when a run may end before the chain stops, and survival is estimated with a discrete Kaplan–Meier estimator.Runs are censored if every agent is infected at the end or the run ends within three time steps of the deepest hop.Source: §5.3 · p. 7
  • A constant per-hop continuation probability p is fit to surviving runs, and the estimated half-life is expressed as log(1/2) / log p̂.The continuation model is S(h) = S(1) p^(h−1); confidence intervals are obtained by bootstrapping universes.Source: §5.3 · p. 7

6 EXPERIMENTS

The experiments test whether a frozen attack can propagate across assistants with persistent memory as they exchange artifacts. Results show spread across models and hand-offs, with depth affected by model behavior, artifact exposure, and the simulated workflow.

6.1 EXPERIMENTAL SETUP

  • The evaluation uses four target models in an OpenClaw harness with private workspaces, persistent MEMORY.md files, and one seed artifact per run.Attack instructions were optimized on 12 development universes and tested on 36 unseen universes, with each universe run twice per model.Source: §6.1 · p. 7

6.2 RESULTS

  • DeepSeek-V4-Pro and GPT-OSS-120B goal-infect 98% and 85% of assistants, respectively; 76% and 61% of their chains reach hop 4.The held-out test-universe results are averaged over repeated runs within each universe and then across universes.Source: Table 1 · p. 7, §6.2 · pp. 7–8
  • After censoring correction, 85% of DeepSeek-V4-Pro chains and 73% of GPT-OSS-120B chains remain alive at hop 7.Most DeepSeek-V4-Pro runs ended before their chains did: 65 of 69 were censored.Source: §6.2 · p. 8, Figure 2 · p. 8
  • Conversion after reading an infected artifact ranges from 0.69 for GPT-5.6 Luna to 1.00 for DeepSeek-V4-Pro, while clean reads encounter infected artifacts in 0.18–0.55 of cases.Fewer infected artifacts are written than goals retained, limiting later encounters.Source: §6.2 · p. 8, Figure 3 · p. 8
  • In 30-agent universes, the attack reaches 90–100% of GPT-OSS-120B and DeepSeek-V4-Pro agents, 77–97% of Kimi-K2.6 agents, and 60–80% of GPT-5.6 Luna agents.Chains reach hop 8; growth is about one new agent every two time steps, and Luna leaves an uninfected periphery in every universe.Source: §6.2 · pp. 8–9, Figure 4 · p. 9
  • Across large-universe runs, 53% of infected agents infect no one, while the most prolific 20% cause 69% of transmissions.One DeepSeek-V4-Pro artifact directly infects 14 agents over 41 steps and accounts for 22 of its run’s 30 infections.Source: §6.2 · p. 9, Figure 5 · p. 10, Figure 6 · p. 10
  • Across all universes, 96–100% of infected assistants send artifacts to the attacker’s endpoint; GPT-5.6 Luna required 4,002 development jobs, while Grok 4.6 search stopped for cost after 571 jobs.Grok 4.6’s second-hop survival was approximately 0.2 when its search stopped; failure within the tested budget should not be taken as evidence of immunity.Source: §6.2 · pp. 9–10

7 DISCUSSION

Artifact-mediated attacks can reach assistants the attacker never accessed, and infected artifacts can sustain spread even after an assistant’s memory is reset. The discussion identifies workflow capabilities that enable the risk and describes defenses at multiple stages.

Why this attack is dangerous

  • A memory reset does not end an outbreak because infected artifacts can reinfect assistants, while the agent—not the endpoint—carries out replication.The agent remembers the goal, writes it into a later artifact, initiates the upload, and checks the returned file; the endpoint reformats content the agent already produced.Source: §7 · p. 11
  • Attackers need one seed artifact read by one assistant, but must keep their external service reachable; the attack can reach assistants they never accessed.The goals range from harmless preferences to repeatedly running attacker-chosen software.Source: §7 · p. 11

From propagation to loss of control

  • As assistants act with more autonomy and less review, adversarial state could spread faster than people can audit what assistants remember.The discussion cites self-copying injections during adversarial training, 506 AI-reader-targeted posts on Moltbook within 72 hours, and agents operating inside another organization’s infrastructure for days.Source: §7 · p. 11

Workflow assistants

  • The attack relies on persistent memory, file reading and writing, and external-service access, but the evaluated products’ internal safeguards were not assessed.In OpenClaw, memory writes are unscreened and outbound access to public hosts is enabled by default.Source: §7 · p. 11

Practical defenses

  • A memory-write classifier flagged all infected memories from the main template with 4/24 false positives; outbound-host allowlists, upload confirmation, and screening widely shared artifacts are other proposed defenses.The evaluated harness applies none of these checks by default.Source: §7 · p. 11

8 CONCLUSION

The paper formalizes artifact-mediated propagation and evaluates it across four models and 36 held-out human–agent workflows. Its findings support treating shared artifacts as part of the security boundary.

8 CONCLUSION

  • A single poisoned artifact triggered multi-hop propagation, reaching 60–100% of agents across the evaluated models.Infected artifacts continued exposing new assistants long after they were created.Source: §8 · p. 11
  • The paper formalizes artifact-mediated propagation and evaluates it across four models and 36 held-out human–agent workflows.Source: §8 · p. 11
  • Shared artifacts should be treated as part of the security boundary because they can preserve and propagate adversarial state across otherwise independent assistants.Source: §8 · p. 11

A LIMITATIONS

The paper evaluates propagation through artifact-sharing workflows and reports prompt-only and security-critical results alongside limits on the evaluated mechanisms, models, and networks. Its simulated universes model user-directed file exchange between assistants without direct agent communication.

A LIMITATIONS

  • Endpoint-assisted propagation requires an infected assistant to use an attacker-controlled service and accept its returned artifact; egress restrictions or rejecting that artifact interrupt the evaluated mechanism.Payload restoration also means infection and hop-survival estimates do not measure unaided reproduction of the full attack prompt.Source: A LIMITATIONS · p. 16

B.1 TEST UNIVERSES

  • The test set contains 36 universes, 274 people, and 889 personal files; busier workload levels add people, time steps, and sharing partners while graph density stays near 0.5.Each universe is assigned one adversarial goal, and the active fraction is highest in the Peak workload level.Source: §B.1 · p. 17, Table 2 · p. 17

B.2 LARGE UNIVERSES

  • The three large universes each contain 30 people and run for 60 time steps with about 190 tasks, using scheduled artifact hand-offs and overlapping social circles.Their structure is controlled so agents are reachable through multiple artifacts, while outbreaks are designed not to die out or saturate trivially.Source: §B.2 · pp. 17–18

C ATTACK TEMPLATES

  • The released GPT-OSS-120B template disguises an adversarial goal as a persistent preference and instructs assistants to reproduce it and upload deliverables to an attacker endpoint.Templates for other evaluated models are available to researchers upon specific request.Source: C ATTACK TEMPLATES · p. 18

D PROMPT-ONLY PROPAGATION

  • 21% of assistants were fully infected by one seed (FIFa = 0.21), with observed S(2) = 0.30, S(3) = 0.12, and S(4) = 0.05.Censoring-corrected estimates were Ŝ(2) = 0.35 and Ŝ(3) = 0.18. Omission, rather than paraphrase, dominated decay: payload drop rates rose from 32% at the first hop to roughly 65% at the second and third; domain S(2) ranged from 0.50 in healthcare-operations to 0.10–0.20 in personal and social worlds.Source: Table 3 · p. 20, D PROMPT-ONLY PROPAGATION · p. 20

A LIMITATIONS

  • Evidence is bounded by one-model, modest-budget endpoint-free testing, limited target-model search, and synthetic sharing graphs.The endpoint-free results are a lower bound at the tested search budget; the strongest available frontier assistants were not attempted, and the sharing networks were synthetic.Source: A LIMITATIONS · p. 16, D PROMPT-ONLY PROPAGATION · p. 21

E PROPAGATION OF SECURITY-CRITICAL GOALS

  • All seven metrics were equal to or lower under security-critical goals, with model rankings unchanged; GPT-5.6 Luna’s S(1) fell from 0.80 to 0.56 and S(4) from 0.24 to 0.08.Security-critical goals propagated somewhat less readily, while three of four models still goal-infected at least one further assistant in most runs.Source: E PROPAGATION OF SECURITY-CRITICAL GOALS · pp. 21–22, Table 5 · p. 22

F ATTACK EFFORT AND FRONTIER-CLASS MODELS

Search effort varies sharply by model: some frozen templates meet the propagation threshold quickly, while GPT-5.6 Luna requires much more search and Grok 4.6 remains below threshold when evaluation stops. The authors conclude that robustness raises the cost of finding a propagating template rather than preventing one.

Weaker models

  • GPT-OSS-120B met the stopping rule after 109 jobs and $1.71 of target-model calls, and Kimi-K2.6 did so after 372 jobs and $33.18.For these models, the authors attribute propagation to how readily they follow the template rather than to search quality.Source: F ATTACK EFFORT AND FRONTIER-CLASS… · p. 22, Figure 7 · p. 23

Provider-dependent behavior

  • DeepSeek-V4-Pro reached high S(2) early, but its template behaved inconsistently across inference providers, so the search continued through 932 jobs to find more consistent candidates.The running S(2) fell below its early level during exploration and recovered after more consistent templates were found.Source: F ATTACK EFFORT AND FRONTIER-CLASS… · pp. 22, 25, Figure 7 · p. 23

Robust and frontier-class models

  • GPT-5.6 Luna required 4,002 jobs and $198.58 of target-model calls to meet the stopping rule, while Grok 4.6 reached S(2) of about 0.2 after 571 jobs and $58.97 but remained below it.The Grok search was stopped before the threshold was met; the authors exclude it from the main evaluation.Source: Table 6 · p. 22, F ATTACK EFFORT AND FRONTIER-CLASS… · p. 25

Frontier-class model

  • Grok 4.6’s S(2) rose from near zero to about 0.2 before leveling off; the authors stopped the search and did not verify whether longer search would reach the threshold.They expected a continued search might produce a rise similar to GPT-5.6 Luna’s, but did not test this.Source: Figure 8 · p. 24, F ATTACK EFFORT AND FRONTIER-CLASS… · p. 25

Implications

  • The authors conclude that robustness raises the cost of finding a propagating template rather than preventing it, and that propagation after infection depends mainly on how often infected artifacts are encountered.They describe the effort as small relative to a well-funded adversary’s resources.Source: F ATTACK EFFORT AND FRONTIER-CLASS… · p. 25

G EFFECT OF DOMAIN AND WORKLOAD

Across domains and workload tiers, model and individual-workflow differences outweigh category-level patterns, and the reported group differences are not interpreted causally. In larger descriptive universes, attacks reach most agents across models and can travel through long chains.

G EFFECT OF DOMAIN AND WORKLOAD

  • Pooled domain infection rates ranged from 0.62 in healthcare operations to 0.69 in software engineering, while model differences were larger and conversion stayed between 0.80 and 0.88.DeepSeek-V4-Pro infected 0.96–1.00 of agents in every domain, whereas GPT-OSS-120B infected 0.81–0.95.Source: G EFFECT OF DOMAIN AND WORKLOAD · p. 25, Figure 9 · p. 26
  • Pooled infection rates ranged from 0.61 to 0.73 across workload tiers with no monotone trend, and the Peak tier confounds workload with group size and duration.GPT-OSS-120B and DeepSeek-V4-Pro infected 0.81–1.00 of agents at every workload level.Source: G EFFECT OF DOMAIN AND WORKLOAD · p. 25, Figure 9 · p. 26
  • For GPT-5.6 Luna and Kimi-K2.6, infection within a single universe ranged from 0 to 1 within one workload tier, and the authors attribute spread more to the particular workflow than to domain or workload level.They do not interpret group differences causally.Source: G EFFECT OF DOMAIN AND WORKLOAD · p. 25

H.1 SETUP

  • The test universes contain at most 12 agents and 20 time steps; the larger appendix universes contain 30 people and assistants and run for 60 steps with about 190 tasks.The larger runs use three seed artifacts and one run per universe, so the authors treat them as descriptive and do not compare them directly with Table 1.Source: H PROPAGATION IN LARGE UNIVERSES · p. 26, §H.1 · pp. 26–27

H.2 OUTBREAK SIZE AND SPEED

  • 90–100% of agents were infected for GPT-OSS-120B and DeepSeek-V4-Pro, 77–97% for Kimi-K2.6, and 60–80% for GPT-5.6 Luna in the large universes.Deepest chains reached 7–8 hops for GPT-OSS-120B and DeepSeek-V4-Pro, 5–7 for Kimi-K2.6, and 3–8 for GPT-5.6 Luna.Source: §H.2 · p. 27, Figure 10 · p. 27

H.3 CONTACT-NETWORK STRUCTURE

In large-universe runs, outbreaks are often carried by a dominant lineage or persistent artifact, while transmission is uneven and depends on exposure opportunities. Memory retains infected content reliably, but re-expression into new artifacts is less consistent.

H.3 CONTACT-NETWORK STRUCTURE

  • In 10 of 12 runs, one seed lineage accounts for more than half of infections, with a median share of 73%; four of 36 seed agents never convert, yet every run produces an outbreak.Across runs, lineages account for 58–100% of infected agents.Source: §H.3 · p. 27, Figure 11 · p. 28
  • An infected seed artifact exposed three later readers after its first reader resisted; those readers and their downstream lineages accounted for 25 of 28 infections in that run.The artifact remained in circulation, so the resisting assistant did not remove the carrier.Source: §H.3 · pp. 28–29, Figure 13 · p. 29
  • 256 of 286 infected non-seed agents (90%) had read no artifact judged infected before the read that infected them, while network position mainly determines when an agent is first exposed.Among agents with at least five turns, 93% of high-degree and 92% of low-degree agents were eventually infected.Source: §H.3 · p. 29
  • 53% of infected agents never infected anyone, while the most prolific 20% accounted for 69% of attributed transmissions; GPT-5.6 Luna showed weaker concentration, with no agent infecting more than three others.The three largest spreaders directly infected 19, 16, and 12 agents; the authors suggest screening artifacts from the most widely read agents could prevent a disproportionate share of spread.Source: §H.3 · pp. 29–30
  • The deepest attributed chain reached hop 8 in a Kimi-K2.6 run, and that lineage infected 20 of the run’s 23 infected agents.The chain ran from step 5 to step 44, with transmissions taking 2–8 steps each.Source: §H.3 · p. 30, Figure 14 · p. 30
  • Early reproduction numbers were 1.3–2.1, then fell below 1 as uninfected agents became scarce; Kimi-K2.6 and DeepSeek-V4-Pro had dispersion estimates k = 0.37 and 0.62.The authors note these estimates are indicative rather than precise because they use three runs per model and 19–30 early infections, with timing-based parent attribution.Source: §H.3 · pp. 30–31, Figure 15 · p. 31, Table 7 · p. 31

I.1 EXFILTRATION OF ARTIFACTS; I.2 PERSISTENCE AND RE-EXPRESSION IN MEMORY

  • 96–100% of infected agents sent at least one artifact to the endpoint, while memory retained both the belief and propagation instruction at approximately 1.0 across lags up to 15 steps.Across all writes, the belief was reproduced in 0.69 of artifacts and the instruction in 0.68 counting paraphrases, but only 0.55 verbatim.Source: §I.1 · p. 32, §I.2 · p. 32, Table 8 · p. 32

I.3 THE AGENT AUTHORS THE REPLICATED CONTENT, NOT THE ENDPOINT

The authors argue that infected memory, rather than the endpoint round trip, drives re-expression: agents write the remembered attack into artifacts before the endpoint modifies them. They also report that a generic prompt-injection classifier separated infected from benign memory lists on their test data, with legitimate user instructions as its main weakness.

I.3 THE AGENT AUTHORS THE REPLICATED CONTENT, NOT THE ENDPOINT

  • In the logged example, the agent’s own memory write already contained the callback goal and carry-forward instructions before any endpoint call, and its later artifact reproduced that state.The authors report that all runs followed a similar pattern: agents wrote the goal and endpoint instructions to memory, then added them to a new artifact and made the call.Source: §I.3 · pp. 33–34
  • The agent checked the returned file against its persisted memory and accepted the block because it recognized it as its own; the authors identify memory infection as the key step in propagation.They say an unfamiliar instruction returned to an uninfected agent would fail the same reconciliation check.Source: §I.3 · p. 34

J SCREENING MEMORY WRITES WITH A PROMPT-INJECTION CLASSIFIER

  • A cheap off-the-shelf classifier separated infected from benign memory lists perfectly on the test data, but also flagged legitimate user instructions; asking about provenance largely removed that weakness.The test used memories from 36 universes and 1,627 judged memory writes, alongside 2,596 benign seed memories.Source: J SCREENING MEMORY WRITES WITH A… · p. 34, §J.1 · p. 34

J.1 SETUP

  • The classifier scored plain-bullet memory lists with a single generic yes-or-no question and flagged a list at probability 0.5 or higher.Repeated runs had per-item standard deviation at most 0.023 and no threshold flips.Source: §J.1 · pp. 34–35

J.2 RESULTS

J.2 finds instruction-bearing memories are detected reliably, but instruction-free false beliefs evade detection. The related-work comparison distinguishes document-task handoffs across private-memory assistants from transmission through shared state or agent infrastructure.

J.2 RESULTS

  • 104 infected lists and 104 benign seed lists are separated perfectly, with a wide margin between the lowest infected and highest benign score.The evaluation spans all 36 universes and goals.Source: §J.2 · p. 35
  • Across twelve template styles, 11 are flagged, but a goal-only note without an instruction is missed at 0.31–0.33.A style-sheet framing is borderline at 0.50–0.55.Source: §J.2 · p. 35
  • Without the instruction block, false-belief scores are 0.02–0.09 in four of five cases, so instruction screening does not detect goal infection alone.One case is an outlier at 0.85.Source: §J.2 · p. 35
  • Fourteen judge-clean memories contain the full injected payload, showing authorship-based propagation labels should not be reused directly as detector training labels.The judge labels whether the agent authored content during the current interaction; the classifier assesses content.Source: §J.2 · pp. 35–36
  • The generic question flags all 24 legitimate imperative entries, while asking about outside provenance reduces benign-memory false positives to 4/24 and retains 1.000 recall on the main template.The remaining errors are data-handling checklists; 11 of 12 template styles are still caught.Source: §J.2 · p. 36

K COMPARISON WITH CLOSELY RELATED WORK

  • The comparison separates persistence within one assistant, shared-state effects across users, and worm transmission via messages or infrastructure from document-task handoffs across assistants with private memories.The table’s final column describes the evaluated payload or template and does not equate task transfer with transfer across adversarial goals.Source: K COMPARISON WITH CLOSELY RELATED WORK · p. 36, Table 10 · p. 37

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