This canonical paper page includes Commercialization Proof and Related Resources.
ARXIV:2603.08616 · FUZZING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.08616FUZZINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Automated fuzz harness generation for Java libraries using LLM-powered agents, improving coverage and bug discovery.
Opportunity summary
Pain Automated fuzz harness generation for Java libraries using LLM-powered agents, improving coverage and bug discovery.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Automated fuzz harness generation for Java libraries using LLM-powered agents, improving coverage and bug discovery. Manual harness creation is time-consuming and requires deep understanding of API semantics, initialization sequences, and exception handling contracts.
Coverage-guided fuzzing has proven effective for software testing, but targeting library code requires specialized fuzz harnesses that translate fuzzer-generated inputs into valid API invocations. Manual harness creation is time-consuming and requires deep understanding of…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To enable effective refinement, we introduce method-targeted coverage that tracks coverage only during target method execution to isolate target behavior, and agent-guided termination that…
Fuzzing moved forward this cycle; last verified April 2026. Public score 8.0/10.
Continue into Read for claims, analysis, references, and neighboring papers.
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
Automated fuzz harness generation for Java libraries using LLM-powered agents, improving coverage and bug discovery.
Loading BUILD…
Paper Pack
10.48550/arXiv.2603.08616Automated fuzz harness generation for Java libraries using LLM-powered agents, improving coverage and bug discovery.
Abstract
Coverage-guided fuzzing has proven effective for software testing, but targeting library code requires specialized fuzz harnesses that translate fuzzer-generated inputs into valid API invocations. Manual harness creation is time-consuming and requires deep understanding of API semantics, initialization sequences, and exception handling contracts. We present a multi-agent architecture that automates fuzz harness generation for Java libraries through specialized LLM-powered agents. Five ReAct agents decompose the workflow into research, synthesis, compilation repair, coverage analysis, and refinement. Rather than preprocessing entire codebases, agents query documentation, source code, and callgraph information on demand through the Model Context Protocol, maintaining focused context while exploring complex dependencies. To enable effective refinement, we introduce method-targeted coverage that tracks coverage only during target method execution to isolate target behavior, and agent-guided termination that examines uncovered source code to distinguish productive refinement opportunities from diminishing returns. We evaluated our approach on seven target methods from six widely-deployed Java libraries totaling 115,000+ Maven dependents. Our generated harnesses achieve a median 26\% improvement over OSS-Fuzz baselines and outperform Jazzer AutoFuzz by 5\% in package-scope coverage. Generation costs average \$3.20 and 10 minutes per harness, making the approach practical for continuous fuzzing workflows. During a 12-hour fuzzing campaign, our generated harnesses discovered 3 bugs in projects that are already integrated into OSS-Fuzz, demonstrating the effectiveness of the generated harnesses.
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 8.0
PROBLEM
Automated fuzz harness generation for Java libraries using LLM-powered agents, improving coverage and bug discovery. Manual harness creation is time-consuming and requires deep understanding of API semantics, initialization sequences, and exception handling contracts.
METHOD
Coverage-guided fuzzing has proven effective for software testing, but targeting library code requires specialized fuzz harnesses that translate fuzzer-generated inputs into valid API invocations. Manual harness creation is time-consuming and requires deep understanding of API s...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To enable effective refinement, we introduce method-targeted coverage that tracks coverage only during target method execution to isolate target behavior, and agent-guided termination that examines uncove...
WHY NOW
Fuzzing moved forward this cycle; last verified April 2026. Public score 8.0/10.
We present a multi-agent architecture that automates fuzz harness generation for Java libraries through specialized LLM-powered agents. Five ReAct agents decompose the workflow into research, synthesis, compilation repair, coverage analysis, and refinement.
Explicitly stated in abstract with clear description of agent roles
partial
Our generated harnesses achieve a median 26% improvement over OSS-Fuzz baselines
Directly stated in abstract with specific numeric result
partial
outperform Jazzer AutoFuzz by 5% in package-scope coverage
Directly stated in abstract with specific numeric comparison
partial
we introduce method-targeted coverage that tracks coverage only during target method execution to isolate target behavior
Explicitly stated as a technical innovation in the abstract
partial
Generation costs average $3.20 and 10 minutes per harness, making the approach practical for continuous fuzzing workflows
Directly stated with specific cost and time metrics
partial
During a 12-hour fuzzing campaign, our generated harnesses discovered 3 bugs in projects that are already integrated into OSS-Fuzz
Directly stated with specific bug discovery results
partial
Manual harness creation is time-consuming and requires deep understanding of API semantics, initialization sequences, and exception handling contracts
Directly stated as motivation for the work
partial
agent-guided termination that examines uncovered source code to distinguish productive refinement opportunities from diminishing returns
Explicitly stated as a technical innovation in the abstract
partial
Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.
Concepts
Methods
Materials
Markets
Competitors
Automated fuzz harness generation for Java libraries using LLM-powered agents, improving coverage and bug discovery.
Segment
Fuzzing
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
Adjacent
Substitute
Unknown
No indexed public discussion is attached to 2603.08616 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
Preview the source document here, or use the hero PDF action for a new tab.
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.
Extension
Commercially relevant
Conflicting
Owned Distribution
Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.
0/3 checks · 0%
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
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
Save this paper to start tracking momentum - commits, demos, and score changes appear here.
No tracked events yet.
Score trend will appear after multiple data points.
BUZZ
Buzz trend pending.