BACO - Bug Analysis & Cross-reference Orchestrator

A CLI-based security vulnerability scanner that combines static analysis, LLM-powered discovery, and ticket system cross-referencing.
Example Report generated with Regolo.AI models on ins1gn1a/VulnServer-Linux.

Features

• Multi-phase scanning: 13+ phases including Indexing → Semgrep → LLM Static Analysis → LLM Discovery → LLM Verification → SecurityAgent Verification → Ticket Cross-Ref → Git Analysis → Cross-File Analysis → Confidence Scoring → AI Aggregation → Reporting → Advanced V3 features (Threat Modeling, CVE Bootstrap, PoC Compilation, Variant Search)
• Parallel execution: Semgrep and LLM discovery run concurrently; verification, ticket cross-ref, and git analysis run in parallel
• Checkpoint/resume: Automatically saves state after each phase for crash recovery
• Multiple output formats: JSON, HTML, SARIF
• Config-driven: TOML configuration with environment variable overrides
• Prompt customization: Override default LLM prompts per phase via config
• Ticket integration: GitHub, GitLab, Bugzilla, Jira support
• Cross-file analysis: Traces data flow between files to identify exploitable chains
• Composite confidence scoring: Combines multiple signals into a single reliability score

Architecture

Pipeline Phases

Core Pipeline (11 phases):

1. Indexing: Build file list and call graph
2. Semgrep: Static analysis with predefined rules
3. LLM Static Analysis: Independent LLM-based code analysis (uses discovery config)
4. LLM Discovery: Multi-model vulnerability detection (all configured models analyze each finding)
5. LLM Verification: Validation with PoC generation and mitigation code
6. SecurityAgent Verification: Tool-based agent verification using file_read, pattern_search, file_write, run_test to confirm true positives
7. Ticket Cross-Ref: Search GitHub/GitLab for existing reports
8. Git Analysis: Check commit history for related fixes
9. Cross-File Analysis: Trace data flow between files
10. Confidence Scoring: Calculate composite reliability score
11. AI Aggregation: Generate executive summary, semantic deduplication, and LLM-enriched descriptions
12. Reporting: Generate JSON, HTML, and SARIF outputs
13. Threat Modeling: Generate THREAT_MODEL.md with attack surface analysis
14. Root Cause Dedup: Deduplicate findings by root cause instead of location
15. Multi-Verifier: Multiple verification methods with majority voting
16. Auto-Patching: Generate and validate patches with staging
17. CVE Bootstrap: Enrich findings with NVD/CISA KEV data
18. PoC Compiler: Verify PoC code compiles successfully
19. Variant Search: Search for related vulnerability variants

Data Flow

Config → Indexing → [Semgrep + LLM Static Analysis + LLM Discovery] → [LLM Verification + SecurityAgent Verification + Tickets + Git + Confidence] → Cross-File → AI Aggregation → Reporting → [Threat Modeling, CVE, PoC, Variants] → JSON/HTML/SARIF Output
                         ↑ Checkpoint after each major stage

Installation

cargo build --release
./target/release/baco --version

Usage

1. Create Configuration

cp config.example.toml myproject.toml

Edit myproject.toml:

• Set project.path to the target directory
• Configure LLM API keys (or use environment variables)
• Set up ticket system credentials if needed

2. Run Scan

baco scan --config myproject.toml

Options:

• -c, --config <FILE> - Configuration file (required)
• -t, --target <PATH> - Override target path from config
• -f, --force - Force fresh scan, ignore existing checkpoint

Resume previous scan:

baco scan --config myproject.toml --force

Use --force to start fresh and ignore the checkpoint file.

3. View Results

output/report.html

Configuration

Project Settings

[project]
name = "my-project"
path = "/path/to/target"
languages = ["c", "cpp", "python"]

LLM Configuration

BACO supports single or multiple models per phase. When multiple models are configured, they are used in round-robin fashion to distribute load across different models/providers.

Detailed error logging: When LLM requests fail, BACO reports the HTTP status code, error type (timeout, connection, request, body, decode), and the actual URL for easier debugging.

Single model:

[llm.phases.discovery]
base_url = "https://api.mistral.ai/v1"
api_key = "${MISTRAL_API_KEY}"  # or set env var
model = "mistral-small"

Multiple models:

[llm.phases.discovery]
base_url = "https://api.mistral.ai/v1"
api_key = "${MISTRAL_API_KEY}"
# 'models' takes precedence over 'model' if both are present
models = ["mistral-small", "mistral-medium", "codestral-latest"]

[llm.phases.verification]
base_url = "https://api.qwen.ai/v1"
api_key = "${QWEN_API_KEY}"
model = "qwen35"  # single model

[llm.phases.aggregation]
base_url = "https://api.openai.com/v1"
api_key = "${OPENAI_API_KEY}"
models = ["gpt-4o", "gpt-4o-mini"]  # multiple models for distributed load

Note: The models array takes precedence over model if both are present. Models are selected in round-robin fashion to distribute load across different providers.

Agent Mode

BACO has two distinct agent modes:

1. Discovery Agent (agent.enabled = true)

When enabled, the LLM Discovery phase reads source files directly before analyzing findings:

[agent]
enabled = true
max_turns = 10           # Max conversation turns with tools
tool_timeout_secs = 60   # Timeout for tool execution
keep_artifacts = false   # Keep generated test files

Benefits:

• LLM reads actual source code before enriching findings
• Uses tools (file_read, pattern_search) for deeper analysis
• Provides more accurate vulnerability descriptions with context

2. SecurityAgent Verification (Phase 6)

A separate verification phase that uses an embedded security agent with tools to prove or disprove findings:

• file_read: Examine vulnerable code in context
• pattern_search: Look for related vulnerability patterns
• file_write: Create proof-of-concept test cases
• run_test: Execute tests to verify exploitability

The agent automatically removes false positives when tests pass, reducing noise in the final report. This phase runs after LLM Verification and before Ticket Cross-Reference.

Prompt Customization

BACO uses prompt templates for each phase loaded from markdown files at runtime. You can override these via configuration:

Default prompts are stored in prompts/phases/ as markdown files:

• prompts/phases/indexing.md
• prompts/phases/semgrep.md
• prompts/phases/llm_static_analysis.md
• prompts/phases/llm_discovery.md
• prompts/phases/llm_verification.md
• prompts/phases/ticket_crossref.md
• prompts/phases/git_analysis.md
• prompts/phases/cross_file_analysis.md
• prompts/phases/confidence_scoring.md
• prompts/phases/ai_aggregation.md
• prompts/phases/reporting.md

View the full prompt templates on GitHub to understand default behavior.

Inline override in config.toml:

[llm.phases.prompt_overrides.phases]
llm_static_analysis = """Analyze this %%LANGUAGE%% code for security vulnerabilities.
Focus on: memory safety, injection risks, and insecure API usage.

File: %%FILE_PATH%%
Code:
%%CODE_CONTENT%%
"""

llm_discovery = """Given this finding, determine if it's a true vulnerability:
Title: %%FINDING_TITLE%%
Location: %%FILE_PATH%%:%%LINE_NUMBER%%
Description: %%VULNERABILITY_DESCRIPTION%%
"""

Available template variables:

• %%PROJECT_PATH%% - Target project path
• %%FILE_EXTENSIONS%% - Detected file extensions
• %%LANGUAGES%% - Target languages
• %%CODE_CONTENT%% - Code snippet being analyzed
• %%LANGUAGE%% - Programming language of the file
• %%FILE_PATH%% - File path
• %%LINE_RANGE%% - Line numbers
• %%FINDING_TITLE%% - Vulnerability title
• %%VULNERABILITY_DESCRIPTION%% - Description text
• %%FINDINGS_COUNT%% - Total findings count
• %%SCAN_DATE%% - Scan date

From external file:

# In config.toml
prompt_overrides = "prompts.toml"

Create prompts.toml:

[phases]
llm_static_analysis = "Your custom prompt here..."
llm_verification = "Your verification prompt..."

Prompts are validated (max 10,000 characters, no null bytes) before use.

Ticket Systems

[[tickets.systems]]
type = "github"
url = "https://api.github.com"
credentials.token = "${GITHUB_TOKEN}"

Output Formats

• findings.json: Complete vulnerability data with all 16 fields
• report.html: Visual report with severity colors, code snippets, AI summary
• report.sarif: SARIF format for CI/CD integration