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ARXIV:2605.15338 · LLM SECURITY · SUBMITTED 18 MAY · 20:33 UTC · FRESHNESS STALE
ARXIV:2605.15338LLM SECURITYSUBMITTED 18 MAY · 20:33 UTCFRESHNESS STALESidharth Pulipaka · Stanislau Hlebik · Leonidas Raghav · Sahar Abdelnabi · Vyas Raina · Ivaxi Sheth · +1 at arXiv
Sleeper memory poisoning attacks LLM agents by corrupting persistent memory, enabling dormant adversarial influence across conversations.
Opportunity summary
Pain Sleeper memory poisoning attacks LLM agents by corrupting persistent memory, enabling dormant adversarial influence across conversations.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
Sleeper memory poisoning attacks LLM agents by corrupting persistent memory, enabling dormant adversarial influence across conversations. This statefulness introduces a new security risk: adversarial content can corrupt what an assistant remembers and thereby influence…
Large language models are increasingly augmented with persistent memory, allowing assistants to store user-specific information across sessions for personalization and continuity. This statefulness introduces a new security risk: adversarial content can corrupt what an…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. These results show that persistent memory can act as a long-term attack surface across multiple future conversations. A public repository is linked, so build…
LLM Security moved forward this cycle; last verified May 2026. Public score 4.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score4.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
Sleeper memory poisoning attacks LLM agents by corrupting persistent memory, enabling dormant adversarial influence across conversations.
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Paper Pack
10.48550/arXiv.2605.15338Sleeper memory poisoning attacks LLM agents by corrupting persistent memory, enabling dormant adversarial influence across conversations.
Abstract
Large language models are increasingly augmented with persistent memory, allowing assistants to store user-specific information across sessions for personalization and continuity. This statefulness introduces a new security risk: adversarial content can corrupt what an assistant remembers and thereby influence future interactions. We propose and study sleeper memory poisoning, a delayed attack in which an adversary manipulates external context, such as a document, webpage, or repository, to cause the assistant to store a fabricated memory about the user. Unlike conventional prompt injection, the attack can remain dormant and re-emerge across multiple later conversations. We evaluate the full attack pipeline: whether poisoned memories are written, later retrieved, and ultimately used to steer the following conversations. Across stateful LLM assistants, poisoned memories were added up to 99.8% on GPT-5.5 and 95% on Kimi-K2.6. Crucially, among successful retrievals, poisoned memories cause attacker-intended agentic actions in 60-89% of evaluations across models. These results show that persistent memory can act as a long-term attack surface across multiple future conversations.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run linkedA document parse run is attached to this paper.
Proof status
unverified0 refs; 4 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 4.0
PROBLEM
Sleeper memory poisoning attacks LLM agents by corrupting persistent memory, enabling dormant adversarial influence across conversations. This statefulness introduces a new security risk: adversarial content can corrupt what an assistant remembers and thereby influence future in...
METHOD
Large language models are increasingly augmented with persistent memory, allowing assistants to store user-specific information across sessions for personalization and continuity. This statefulness introduces a new security risk: adversarial content can corrupt what an assistant...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. These results show that persistent memory can act as a long-term attack surface across multiple future conversations. A public repository is linked, so build verification can inspect implementation eviden...
WHY NOW
LLM Security moved forward this cycle; last verified May 2026. Public score 4.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
Sleeper memory poisoning attacks LLM agents by corrupting persistent memory, enabling dormant adversarial influence across conversations. This statefulness introduces a new security risk: adversarial content can corrupt what an assistant remembers and thereby influence future interactions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models are increasingly augmented with persistent memory, allowing assistants to store user-specific information across sessions for personalization and continuity. This statefulness introduces a new security risk: adversarial content can corrupt what an assistant remembers and thereby influence future interactions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. These results show that persistent memory can act as a long-term attack surface across multiple future conversations. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Security moved forward this cycle; last verified May 2026. Public score 4.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
Markets
Competitors
Sleeper memory poisoning attacks LLM agents by corrupting persistent memory, enabling dormant adversarial influence across conversations.
Segment
LLM Security
Adoption evidence
Public code linked for build inspection
Commercial read
4.0/10 public viability
Direct
Adjacent
Substitute
Unknown
No indexed public discussion is attached to 2605.15338 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.
Hacker News
Not indexed yet
Not indexed yet
Bluesky
Not indexed yet
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Reference metadata is not materialized in the public index yet. The source PDF remains the authority; cache refresh is optional.
CITED BY
No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.
Foundation
Extension
Commercially relevant
Owned Distribution
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 4 sources / 67% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 4 sources, 67% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
No verified competitive landscape changes yet.
RELATED PAPER UPDATES
No verified related paper changes yet.
SIGNAL CANVAS HISTORY AND DELTAS
No Signal Canvas history deltas yet.
TIMELINE
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No tracked events yet.
Score trend will appear after multiple data points.
BUZZ
Buzz trend pending.