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ARXIV:2602.19818 · SECURITY AND MODEL INTEGRITY · SUBMITTED 17 MAR · 19:46 UTC · FRESHNESS STALE
ARXIV:2602.19818SECURITY AND MODEL INTEGRITYSUBMITTED 17 MAR · 19:46 UTCFRESHNESS STALEarXiv
SafePickle offers a machine learning-based solution to detect malicious Pickle files in model repositories, enhancing security in AI model sharing.
Opportunity summary
Pain SafePickle offers a machine learning-based solution to detect malicious Pickle files in model repositories, enhancing security in AI model sharing.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence failed
SafePickle offers a machine learning-based solution to detect malicious Pickle files in model repositories, enhancing security in AI model sharing. Recent defenses, such as PickleBall, rely on per-library policy synthesis that requires complex system…
Model repositories such as Hugging Face increasingly distribute machine learning artifacts serialized with Python's pickle format, exposing users to remote code execution (RCE) risks during model loading. Recent defenses, such as PickleBall, rely on…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Our method achieves 90.01% F1-score compared with 7.23%-62.75% achieved by the SOTA scanners (Modelscan, Fickling, ClamAV, VirusTotal) on our dataset.
Security and Model Integrity moved forward this cycle; last verified April 2026. Public score 8.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
SafePickle offers a machine learning-based solution to detect malicious Pickle files in model repositories, enhancing security in AI model sharing.
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Paper Pack
10.48550/arXiv.2602.19818SafePickle offers a machine learning-based solution to detect malicious Pickle files in model repositories, enhancing security in AI model sharing.
Abstract
Model repositories such as Hugging Face increasingly distribute machine learning artifacts serialized with Python's pickle format, exposing users to remote code execution (RCE) risks during model loading. Recent defenses, such as PickleBall, rely on per-library policy synthesis that requires complex system setups and verified benign models, which limits scalability and generalization. In this work, we propose a lightweight, machine-learning-based scanner that detects malicious Pickle-based files without policy generation or code instrumentation. Our approach statically extracts structural and semantic features from Pickle bytecode and applies supervised and unsupervised models to classify files as benign or malicious. We construct and release a labeled dataset of 727 Pickle-based files from Hugging Face and evaluate our models on four datasets: our own, PickleBall (out-of-distribution), Hide-and-Seek (9 advanced evasive malicious models), and synthetic joblib files. Our method achieves 90.01% F1-score compared with 7.23%-62.75% achieved by the SOTA scanners (Modelscan, Fickling, ClamAV, VirusTotal) on our dataset. Furthermore, on the PickleBall data (OOD), it achieves 81.22% F1-score compared with 76.09% achieved by the PickleBall method, while remaining fully library-agnostic. Finally, we show that our method is the only one to correctly parse and classify 9/9 evasive Hide-and-Seek malicious models specially crafted to evade scanners. This demonstrates that data-driven detection can effectively and generically mitigate Pickle-based model file attacks.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
failed0 refs; 0 sources; 33% 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 8.0
PROBLEM
SafePickle offers a machine learning-based solution to detect malicious Pickle files in model repositories, enhancing security in AI model sharing. Recent defenses, such as PickleBall, rely on per-library policy synthesis that requires complex system setups and verified benign m...
METHOD
Model repositories such as Hugging Face increasingly distribute machine learning artifacts serialized with Python's pickle format, exposing users to remote code execution (RCE) risks during model loading. Recent defenses, such as PickleBall, rely on per-library policy synthesis...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Our method achieves 90.01% F1-score compared with 7.23%-62.75% achieved by the SOTA scanners (Modelscan, Fickling, ClamAV, VirusTotal) on our dataset.
WHY NOW
Security and Model Integrity moved forward this cycle; last verified April 2026. Public score 8.0/10.
Our method achieves 90.01% F1-score compared with 7.23%-62.75% achieved by the SOTA scanners (Modelscan, Fickling, ClamAV, VirusTotal) on our dataset.
Explicit numeric comparison stated in abstract with clear performance metrics
partial
on the PickleBall data (OOD), it achieves 81.22% F1-score compared with 76.09% achieved by the PickleBall method, while remaining fully library-agnostic.
Direct numeric comparison with competing method on OOD dataset stated in abstract
partial
we show that our method is the only one to correctly parse and classify 9/9 evasive Hide-and-Seek malicious models specially crafted to evade scanners.
Explicit statement of perfect detection rate on challenging evasion dataset
partial
Our approach statically extracts structural and semantic features from Pickle bytecode and applies supervised and unsupervised models to classify files as benign or malicious.
Core methodology clearly described in abstract and analysis section
partial
Recent defenses, such as PickleBall, rely on per-library policy synthesis that requires complex system setups and verified benign models, which limits scalability and generalization.
Direct comparison with limitations of previous work stated in abstract
partial
There could be limitations in handling new types of novel attacks that are not covered in the existing datasets
Explicitly stated as a caveat in the analysis section
partial
false positives could potentially disrupt legitimate workflows if the system isn't tuned properly.
Explicitly stated as a caveat in the analysis section
partial
This demonstrates that data-driven detection can effectively and generically mitigate Pickle-based model file attacks.
Conclusion explicitly stated in abstract based on presented results
partial
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Concepts
Methods
Materials
Markets
Competitors
SafePickle offers a machine learning-based solution to detect malicious Pickle files in model repositories, enhancing security in AI model sharing.
Segment
Security and Model Integrity
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
Adjacent
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CITED BY
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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
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No verified cost estimate
confidence low
next verification path
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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.
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Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 33% 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, 33% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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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
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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
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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
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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People
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Gaps
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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