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ARXIV:2602.22983 · LLM SECURITY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.22983LLM SECURITYSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Exploit weaknesses in LLMs with classical Chinese prompts using an optimization framework for more effective jailbreak attacks.
Opportunity summary
Pain Exploit weaknesses in LLMs with classical Chinese prompts using an optimization framework for more effective jailbreak attacks.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Exploit weaknesses in LLMs with classical Chinese prompts using an optimization framework for more effective jailbreak attacks. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts.
As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts.
ScienceToStartup currently rates this 6.0/10 on the public viability pass. This design enables efficient exploration of the search space, thereby enhancing the effectiveness of black-box jailbreak attacks.
LLM Security moved forward this cycle; last verified April 2026. Public score 6.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score6.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
Exploit weaknesses in LLMs with classical Chinese prompts using an optimization framework for more effective jailbreak attacks.
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Paper Pack
10.48550/arXiv.2602.22983Exploit weaknesses in LLMs with classical Chinese prompts using an optimization framework for more effective jailbreak attacks.
Abstract
As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts. This paper investigates the role of classical Chinese in jailbreak attacks. Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs. Based on this observation, this paper proposes a framework, CC-BOS, for the automatic generation of classical Chinese adversarial prompts based on multi-dimensional fruit fly optimization, facilitating efficient and automated jailbreak attacks in black-box settings. Prompts are encoded into eight policy dimensions-covering role, behavior, mechanism, metaphor, expression, knowledge, trigger pattern and context; and iteratively refined via smell search, visual search, and cauchy mutation. This design enables efficient exploration of the search space, thereby enhancing the effectiveness of black-box jailbreak attacks. To enhance readability and evaluation accuracy, we further design a classical Chinese to English translation module. Extensive experiments demonstrate that effectiveness of the proposed CC-BOS, consistently outperforming state-of-the-art jailbreak attack methods.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% 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 6.0
PROBLEM
Exploit weaknesses in LLMs with classical Chinese prompts using an optimization framework for more effective jailbreak attacks. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts.
METHOD
As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts.
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. This design enables efficient exploration of the search space, thereby enhancing the effectiveness of black-box jailbreak attacks.
WHY NOW
LLM Security moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Exploit weaknesses in LLMs with classical Chinese prompts using an optimization framework for more effective jailbreak attacks. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. This design enables efficient exploration of the search space, thereby enhancing the effectiveness of black-box jailbreak attacks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Security moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
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Exploit weaknesses in LLMs with classical Chinese prompts using an optimization framework for more effective jailbreak attacks.
Segment
LLM Security
Adoption evidence
No public code link in the paper record yet
Commercial read
6.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Foundation
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Commercially relevant
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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 / 0 sources / 17% 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, 0 sources, 17% 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
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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
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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
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.