Baco's LLM integration is informed by a survey of 36 papers from Awesome-LLMs-for-Vulnerability-Detection. This document details the 16 papers integrated into baco's architecture and what each contributes.
This document covers the 16 papers already integrated into baco's architecture — what each contributes, where it's wired in, and the config flag that enables it.
For the broader survey of 36 surveyed papers and the selection criteria for the P1–P5 integration tracks, see Paper Survey.
Sifting the Noise (ISSTA 2026) — arxiv
Problem: LLM-based vulnerability detectors produce excessive false positives, with initial detection rates showing up to 92% false positive rates that overwhelm developers.
Technique: A lightweight agentic triage system filters findings before final reporting. The approach uses a minimal LLM call to validate each potential vulnerability, applying learned heuristics to distinguish true positives from noise without expensive re-analysis.
Key Result: False positive rate dropped from 92% to 6.3% with the triage filter, representing a dramatic improvement in signal quality while maintaining high recall.
Baco Integration: Baco implements a triage filter phase before final vulnerability reporting, applying the same lightweight validation pattern to reduce noise in the output.
AutoCVE — code
Problem: Vulnerability detection pipelines generate redundant findings across multiple scans, requiring manual deduplication and causing alert fatigue.
Technique: A four-agent pipeline handles reconnaissance, scanning, triage, and finding consolidation. Each agent has a specialized role, with the triage agent performing cross-scan deduplication and the finding agent producing consolidated reports.
Key Result: The multi-agent approach achieves significant deduplication rates while preserving true positive coverage, with each agent operating independently to avoid cascading failures.
Baco Integration: Baco adopts the multi-agent deduplication pattern as a global false positive suppression stage, consolidating findings across aggregation boundaries.
Cloudflare Security-Audit-Skill — code
Problem: Security audit workflows suffer from sequential bottlenecks and lack independent verification at each stage.
Technique: A six-phase parallel pipeline with independent verification at each step. The architecture restructures the audit process into a graph of independent phases that can run in parallel, with each phase having its own verification mechanism.
Key Result: The parallel orchestration pattern doubled discovery rates compared to sequential approaches while maintaining accuracy through independent verification.
Baco Integration: Baco restructured its pipeline to a six-step graph with independent verification, implementing the Cloudflare pattern for higher discovery rates.
Context-Enhanced Vulnerability Detection — arxiv
Problem: LLMs analyzing code lack sufficient context to understand vulnerability patterns, leading to poor performance on zero-shot tasks.
Technique: Hierarchical context extraction builds multi-level representations of code, from function-level summaries to cross-file dependencies. Filtered abstracted context consistently outperforms raw code prompts by focusing the LLM on relevant vulnerability patterns.
Key Result: Filtered context injection improved detection accuracy by addressing the zero-shot performance gap, with abstracted representations showing consistent gains across vulnerability types.
Baco Integration: Baco implements multi-level context injection into LLM prompts, using hierarchical extraction to provide relevant context without overwhelming the model.
VulIn (BM25 RAG) — arxiv
Problem: Retrieval-augmented generation for vulnerability detection typically requires expensive vector stores and complex infrastructure.
Technique: Specification RAG with BM25 retrieval uses a JSON-serializable CWE knowledge base. The approach shows that keyword-based retrieval can sharply reduce false positives without the overhead of embedding models or vector databases.
Key Result: Spec RAG with BM25 achieved significant false positive reduction while maintaining a lightweight, serializable knowledge base that integrates cleanly into existing pipelines.
Baco Integration: Baco uses CWE knowledge base RAG with BM25 retrieval for security specifications, avoiding vector stores while maintaining high-quality context.
VulTriage — arxiv
Problem: Vulnerability analysis lacks proper data-flow grounding, causing LLMs to miss critical execution paths and control dependencies.
Technique: Triple-path context augmentation extracts control flow (AST/CFG/DFG), knowledge flow, and semantic flow information. The control path specifically addresses missing data-flow grounding by providing structural program analysis alongside semantic context.
Key Result: Triple-path context extraction significantly improved analysis accuracy by grounding LLM reasoning in actual program structure rather than surface-level patterns.
Baco Integration: Baco implements triple-path context extraction with program analysis grounding, using the control path to address data-flow grounding gaps.
LLMxCPG (Usenix 2025) — arxiv
Problem: LLMs analyzing full codebases face context window limits and performance degradation from excessive code volume.
Technique: CPG-guided slicing uses the Code Property Graph to reduce code volume by 67-91% while preserving vulnerability-relevant paths. The approach complements data-flow analysis and multi-file correlation by providing precise slicing based on program structure.
Key Result: CPG-guided slicing achieved the highest quality gain among context techniques, reducing code volume dramatically while maintaining analysis accuracy.
Baco Integration: Baco integrates CPG-guided slicing using Joern CPG to reduce code volume for LLM analysis while preserving critical vulnerability paths.
MoCQ — arxiv
Problem: Manual rule creation for static analysis is slow and cannot keep pace with emerging vulnerability patterns.
Technique: LLM-generated patterns with symbolic validation synthesize semgrep rules from vulnerability descriptions. The approach uses rapid iteration with build validation to achieve immediate F-score uplift, fitting naturally into cargo-based build systems.
Key Result: LLM-driven rule synthesis achieved rapid F-score improvement through automated pattern generation and validation, demonstrating the viability of synthetic rule creation.
Baco Integration: Baco implements LLM-driven semgrep rule synthesis with validation, using the cargo-based build system for rapid rule iteration.
QRS — arxiv
Problem: Static analysis rules are limited by manual creation and cannot adapt quickly to new vulnerability patterns.
Technique: LLM-to-CodeQL query generation with adversarial validation creates detection queries automatically. The approach includes on-the-fly exploit synthesis to validate generated rules, escaping the limits of manual rule creation.
Key Result: The LLM-to-rule compiler with adversarial validation produced high-quality CodeQL queries that matched or exceeded manually created rules.
Baco Integration: Baco implements adversarial exploit synthesis to validate generated rules, using the QRS pattern for dynamic rule verification.
MoEVD (FSE 2025) — arxiv
Problem: Monolithic LLMs collapse on long-tailed CWEs, failing to detect less common vulnerability patterns.
Technique: Mixture-of-experts architecture routes queries to specialized models per-CWE. The approach fits naturally with phase-dispatch architectures, allowing different models and prompts for different vulnerability types.
Key Result: Per-CWE routing significantly improved detection on long-tailed vulnerabilities while maintaining performance on common patterns.
Baco Integration: Baco implements per-CWE and per-language routing with specialized prompts and models, using the MoEVD pattern for phase-based dispatch.
Problem: Vulnerability explanations lack clarity and root-cause analysis, reducing developer trust in findings.
Technique: Structured reasoning distillation combined with context-aware reinforcement learning produces specialized reasoning models. The approach trains models specifically for vulnerability explanation and root-cause analysis.
Key Result: Specialized reasoning models significantly improved explainability and root-cause analysis quality compared to general-purpose LLMs.
SV-TrustEval-C (SP 2025) — arxiv
Problem: LLM-based vulnerability detectors can regress to pattern-matching behavior without proper evaluation metrics.
Technique: Structure-oriented benchmark with 9,401 Q&A pairs covering 82 CWEs provides comprehensive evaluation coverage. The benchmark alerts when models regress to pattern-matching rather than genuine vulnerability reasoning.
Key Result: The evaluation suite successfully detected regression to pattern-matching in tested models, validating its effectiveness as a quality gate.
Baco Integration: Baco uses SV-TrustEval-C as a regression suite to validate LLM phase quality and detect performance degradation.
CORRECT — arxiv
Problem: LLM-generated vulnerability rationales often contain hallucinations that undermine trust in findings.
Technique: Rationale validation via LLM-as-judge down-ranks findings whose explanations fail consistency checks. The approach uses a secondary LLM to validate the reasoning behind each vulnerability finding.
Key Result: Rationale validation significantly reduced hallucination rates while maintaining true positive coverage.
Baco Integration: Baco implements rationale validation to reduce reasoning errors and hallucinations in vulnerability explanations.
SecVulEval — arxiv
Problem: Vulnerability localization at statement-level granularity is extremely difficult, with best LLMs achieving only 23.83% F1.
Technique: Statement-level localization benchmark provides rigorous evaluation for fine-grained vulnerability detection. The benchmark pushes beyond line-level to statement/block-level precision.
Key Result: The benchmark established that even best LLMs struggle with statement-level localization, highlighting the need for confidence-aware detection.
Baco Integration: Baco implements statement/block-level confidence scoring instead of simple line-level detection, addressing the granularity challenge.
PrimeVul (ICSE 2025) — arxiv
Problem: Legacy vulnerability benchmarks overestimate model performance by 22× due to data contamination and poor hygiene.
Technique: Chronological dataset hygiene enforces time-based splits, deduplication, and cross-context labeling. The approach prevents data leakage by ensuring training data predates vulnerability disclosures.
Key Result: Legacy benchmarks were shown to overestimate performance by 22×, validating the need for chronological hygiene in evaluation.
Baco Integration: Baco implements dataset hygiene with chronological splits and deduplication to ensure accurate performance measurement.
Closing the Gap (ICSE 2025) — arxiv
Problem: High false positive rates and non-applicable fixes destroyed developer adoption of LLM vulnerability detectors.
Technique: Confidence calibration via post-hoc normalization adjusts model outputs to match real-world accuracy. The approach calibrates confidence scores to reflect actual false positive rates.
Key Result: Post-hoc normalization significantly improved calibration, making confidence scores trustworthy for triage decisions.
Baco Integration: Baco implements confidence calibration with post-hoc normalization to produce trustworthy confidence scores.
Papers assessed but deferred or skipped:
| Paper | Verdict | Rationale |
|---|---|---|
| AgentFlow (2604.20801) | Defer | Python-only; revisit once Rust interop stabilizes |
| AgenticSCR (2601.19138) | Defer | Niche pre-commit case; wait for phase loop stabilization |
| VulnLLM-R (2512.07533) | Skip | GPU cluster required; consume as external service if needed |
| OpenAnt | Defer | Heavy UniFFI build; architectural impact too large |
| DeepAudit | Skip | Full platform, diverges from baco's monolithic Rust design |
| FocusVul (2505.17460) | Skip | Paper withdrawn; code inaccessible |
| VULPO (2511.11896) | Defer | Interesting future fine-tuning, not current runtime |
| R2Vul (2504.04699) | Defer | Needs GPU infra; defer to future explainability work |
| Semantic Trap (2601.22655) | Guidance-only | Fine-tuning guidance not applicable; baco uses API models, not fine-tuned models |
All papers and projects surveyed from: