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ARXIV:2604.01645 · SECURITY AND VULNERABILITY · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.01645SECURITY AND VULNERABILITYSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALEFabian Fleischer · Cen Zhang · Joonun Jang · Jeongin Cho · Meng Xu · Taesoo Kim · arXiv
GONDAR identifies and exploits Java vulnerabilities through a novel LLM-assisted fuzzing framework, significantly outperforming existing tools.
Opportunity summary
Pain GONDAR identifies and exploits Java vulnerabilities through a novel LLM-assisted fuzzing framework, significantly outperforming existing tools.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
GONDAR identifies and exploits Java vulnerabilities through a novel LLM-assisted fuzzing framework, significantly outperforming existing tools. These sink APIs encode critical information for vulnerability discovery: the program-specific constraints required to reach them and the…
Java applications are prone to vulnerabilities stemming from the insecure use of security-sensitive APIs, such as file operations enabling path traversal or deserialization routines allowing remote code execution. These sink APIs encode critical information…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Notably, GONDAR also demonstrated strong performance in the DARPA AI Cyber Challenge, and is integrated into OSS-CRS, a sandbox project in The Linux Foundation's…
Security and Vulnerability moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
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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
GONDAR identifies and exploits Java vulnerabilities through a novel LLM-assisted fuzzing framework, significantly outperforming existing tools.
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Paper Pack
10.48550/arXiv.2604.01645GONDAR identifies and exploits Java vulnerabilities through a novel LLM-assisted fuzzing framework, significantly outperforming existing tools.
Abstract
Java applications are prone to vulnerabilities stemming from the insecure use of security-sensitive APIs, such as file operations enabling path traversal or deserialization routines allowing remote code execution. These sink APIs encode critical information for vulnerability discovery: the program-specific constraints required to reach them and the exploitation conditions necessary to trigger security flaws. Despite this, existing fuzzers largely overlook such vulnerability-specific knowledge, limiting their effectiveness. We present GONDAR, a sink-centric fuzzing framework that systematically leverages sink API semantics for targeted vulnerability discovery. GONDAR first identifies reachable and exploitable sink call sites through CWE-specific scanning combined with LLM-assisted static filtering. It then deploys two specialized agents that work collaboratively with a coverage-guided fuzzer: an exploration agent generates inputs to reach target call sites by iteratively solving path constraints, while an exploitation agent synthesizes proof-of-concept exploits by reasoning about and satisfying vulnerability-triggering conditions. The agents and fuzzer continuously exchange seeds and runtime feedback, complementing each other. We evaluated GONDAR on real-world Java benchmarks, where it discovers four times more vulnerabilities than Jazzer, the state-of-the-art Java fuzzer. Notably, GONDAR also demonstrated strong performance in the DARPA AI Cyber Challenge, and is integrated into OSS-CRS, a sandbox project in The Linux Foundation's OpenSSF, to improve the security of open-source software.
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; 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
GONDAR identifies and exploits Java vulnerabilities through a novel LLM-assisted fuzzing framework, significantly outperforming existing tools. These sink APIs encode critical information for vulnerability discovery: the program-specific constraints required to reach them and th...
METHOD
Java applications are prone to vulnerabilities stemming from the insecure use of security-sensitive APIs, such as file operations enabling path traversal or deserialization routines allowing remote code execution. These sink APIs encode critical information for vulnerability dis...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Notably, GONDAR also demonstrated strong performance in the DARPA AI Cyber Challenge, and is integrated into OSS-CRS, a sandbox project in The Linux Foundation's OpenSSF, to improve the security of open-s...
WHY NOW
Security and Vulnerability moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
We evaluated GONDAR on real-world Java benchmarks, where it discovers four times more vulnerabilities than Jazzer, the state-of-the-art Java fuzzer.
Directly stated in the abstract with clear numeric comparison to the state-of-the-art tool.
partial
We present GONDAR, a sink-centric fuzzing framework that systematically leverages sink API semantics for targeted vulnerability discovery.
Explicitly stated as the core method in the abstract; it is the main contribution of the paper.
partial
GONDAR first identifies reachable and exploitable sink call sites through CWE-specific scanning combined with LLM-assisted static filtering.
Directly stated as a key step in the method; it is a specific technical approach.
partial
Dependency on the accuracy of LLMs' semantic reasoning
Explicitly listed as a caveat in the analysis excerpt.
partial
Notably, GONDAR also demonstrated strong performance in the DARPA AI Cyber Challenge
Directly stated in the abstract as an external validation of performance.
partial
and is integrated into OSS-CRS, a sandbox project in The Linux Foundation's OpenSSF, to improve the security of open-source software.
Directly stated in the abstract, indicating real-world adoption and integration.
partial
It then deploys two specialized agents that work collaboratively with a coverage-guided fuzzer: an exploration agent generates inputs to reach target call sites by iteratively solving path constraints, while an exploitation agent synthesizes proof-of-concept exploits by reasoning about and satisfying vulnerability-triggering conditions.
Directly stated in the abstract as a core component of the method.
partial
potential high computational demand for large-scale vulnerability discovery.
Explicitly listed as a caveat in the analysis excerpt, though it is phrased as a potential issue.
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
GONDAR identifies and exploits Java vulnerabilities through a novel LLM-assisted fuzzing framework, significantly outperforming existing tools.
Segment
Security and Vulnerability
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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Bluesky
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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
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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 / 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
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
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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Score trend will appear after multiple data points.
BUZZ
Buzz trend pending.