Security toolkit for MCP servers and AI agents. Static analysis, runtime policy enforcement, DLP, and audit logging -- from development to production.
SpiderShield is a 5-subsystem security toolkit:
| Subsystem | Command / API | What it does |
|---|---|---|
| Static Scanner | spidershield scan |
Score tool descriptions, detect code vulnerabilities, rate overall quality (F/C/B/A/A+) |
| Agent Security | spidershield agent-check |
18 config checks, 15 malicious pattern detections, toxic flow analysis, rug pull detection |
| Runtime Guard SDK | SpiderGuard(policy="balanced") |
Pre/post-execution policy enforcement for tool calls |
| MCP Proxy | guard_mcp_server(cmd) |
Transparent security proxy between agent and MCP server |
| DLP Engine | Built into Guard SDK | Scan tool outputs for PII/secrets, redact or block |
pip install spidershieldRequires Python 3.11+.
spidershield scan ./your-mcp-serverExample output:
SpiderShield Scan Report
modelcontextprotocol/servers/filesystem
+---------------------------------------------+
| Metric | Value | Score |
|-----------------------+-----------+---------|
| License | MIT | OK |
| Tools | 14 | OK |
| Security | 0 issues | 10.0/10 |
| Descriptions | | 3.2/10 |
| Architecture | | 10.0/10 |
| Tests | Yes | OK |
| | | |
| Overall | Rating: B | 7.6/10 |
| Improvement Potential | | 2.4/10 |
+---------------------------------------------+
Enforce security policies on every tool call at runtime:
from spidershield import SpiderGuard, Decision
guard = SpiderGuard(policy="strict")
result = guard.check("read_file", {"path": "/etc/passwd"})
if result.decision == Decision.DENY:
print(result.reason) # "System file access blocked"
print(result.suggestion) # "Use application-level files instead"Policy presets:
| Preset | Behavior |
|---|---|
strict |
Deny by default, explicit allow list |
balanced |
Block known-dangerous patterns, allow common operations |
permissive |
Warn on suspicious patterns, allow most operations |
| Custom YAML | Load your own policy file: SpiderGuard(policy="my-policy.yaml") |
With audit logging and DLP:
guard = SpiderGuard(
policy="strict",
audit=True, # Write audit trail to disk
audit_dir="./logs", # Custom audit directory
dlp="redact", # Scan outputs for PII/secrets, redact matches
)
# Pre-execution check
result = guard.check("query_db", {"sql": "SELECT * FROM users"})
# Post-execution DLP scan
clean_output = guard.after_check("query_db", raw_result)With data flywheel (opt-in telemetry to local SQLite):
guard = SpiderGuard(policy="balanced", dataset=True)
# Every check() call feeds the local dataset for scoring calibrationWrap any MCP server with SpiderShield policy enforcement:
from spidershield import guard_mcp_server
# Proxy between agent and server, enforcing "balanced" policy
guard_mcp_server(
["npx", "server-filesystem", "/tmp"],
policy="balanced",
audit=True,
)Or from the CLI:
spidershield proxy -- npx server-filesystem /tmp --policy balancedSpiderShield can automatically rewrite tool descriptions to be action-oriented, with scenario triggers, parameter examples, and error guidance.
# Preview changes (no files modified)
spidershield rewrite ./your-mcp-server --dry-run
# Apply changes to source files
spidershield rewrite ./your-mcp-serverBefore (score 2.9):
"Shows the working tree status"
After (score 9.6):
"Query the current state of the Git working directory and staging area.
Use when the user wants to check which files are modified, staged, or
untracked before committing."
The rewriter works offline using templates (zero cost). Set ANTHROPIC_API_KEY for higher-quality LLM-powered rewrites.
| Server | Tools | Security | Descriptions | Overall | Rating |
|---|---|---|---|---|---|
| filesystem | 14 | 10.0 | 3.2 | 7.6 | B |
| git | 12 | 10.0 | 2.4 | 7.3 | B |
| memory | 9 | 10.0 | 2.3 | 7.3 | B |
| fetch | 1 | 9.0 | 3.5 | 7.3 | B |
| supabase | 30 | 9.0 | 2.3 | 6.4 | B |
Full report: MCP-SECURITY-REPORT.md | Raw data: CURATION-REPORT.md
The repo includes example MCP servers for instant demo:
git clone https://github.com/teehooai/spidershield
cd spidershield
spidershield scan examples/insecure-server # Rating: D (3.3/10)
spidershield scan examples/secure-server # Rating: D (4.7/10)Security (weighted 35%)
- Path traversal
- Command injection / dangerous eval
- SQL injection (Python + TypeScript)
- SSRF (unrestricted network access)
- Hardcoded credentials
- Unsafe deserialization (pickle, yaml.load)
- Prototype pollution (TypeScript)
Descriptions (weighted 35%)
- Action verb starts ("List", "Create", "Execute")
- Scenario triggers ("Use when the user wants to...")
- Parameter documentation
- Parameter examples
- Error handling guidance
- Disambiguation between similar tools
- Length (too short = vague, too long = noisy)
Architecture (weighted 30%)
- Test coverage (gradual: count-based)
- Error handling (gradual: coverage-based)
- README quality (gradual: length-based)
- Type annotations
- Dependency management
- Environment configuration
License (pass/fail gate, not weighted)
- MIT, Apache-2.0, BSD = OK
- GPL, AGPL = warning
- Missing = fail
Scan AI agent installations for security misconfigurations and malicious skills.
spidershield agent-check ~/.openclawWhat it checks:
- 10 configuration security checks (auth, sandbox, SSRF, permissions, etc.)
- 20+ malicious skill patterns (reverse shells, credential theft, prompt injection)
- Toxic flow detection -- flags skills that can read sensitive data AND send it externally
- Typosquat detection for skill names
- Excessive permission requests
Advanced options:
# Verify skill integrity (rug pull detection)
spidershield agent-check --verify
# Only approved skills allowed
spidershield agent-check --allowlist approved.json
# Strict mode: fail on any finding
spidershield agent-check --policy strict
# Ignore specific rules
spidershield agent-check --ignore TS-W001 --ignore typosquat
# Auto-fix configuration issues
spidershield agent-check --fix
# SARIF output for GitHub Code Scanning
spidershield agent-check --format sarif > results.sarifSkill pinning (rug pull protection):
spidershield agent-pin add ~/.openclaw/skills/my-skill/SKILL.md
spidershield agent-pin add-all
spidershield agent-pin verify # detect tampered skills
spidershield agent-pin list46 standardized issue codes across 4 categories:
| Code | Category | Example |
|---|---|---|
| TS-E001~E015 | Error (malicious) | Reverse shell, credential theft, prompt injection |
| TS-W001~W011 | Warning (suspicious) | Typosquat, toxic flow, unapproved skill |
| TS-C001~C018 | Config | No auth, sandbox disabled, SSRF enabled |
| TS-P001~P002 | Pin | Verified, tampered |
| Rating | Score | Meaning |
|---|---|---|
| A | 8.5+ | Production-ready |
| B | 7.0+ | Safe with minor suggestions |
| C | 5.0+ | Usable, needs improvements |
| D | 3.0+ | Significant issues |
| F | <3.0 | Unsafe, do not deploy |
Formula: description * 0.35 + security_adjusted * 0.35 + architecture * 0.30
spidershield scan ./server --format json
spidershield scan ./server --format json -o report.jsonAdd SpiderShield to your CI pipeline:
- uses: teehooai/spidershield@v0.3.0
with:
target: '.'
fail-below: '6.0'| Command | Description |
|---|---|
spidershield scan <path> |
Scan and rate an MCP server |
spidershield rewrite <path> |
Rewrite tool descriptions |
spidershield harden <path> |
Suggest security hardening (advisory only) |
spidershield eval <original> <improved> |
Compare tool selection accuracy |
spidershield agent-check [dir] |
Scan an AI agent for security issues |
spidershield agent-pin <cmd> |
Manage skill pins for rug pull detection |
spidershield dataset stats |
View data flywheel statistics |
spidershield dataset benchmark-add |
Add a benchmark entry |
spidershield dataset benchmark-run |
Re-run benchmarks |
spidershield dataset calibrate |
Run scoring calibration |
SpiderShield provides both static analysis and runtime policy enforcement.
What it catches:
- Ambiguous tool definitions that lead to agent misuse
- Missing side-effect declarations (writes, deletes, network calls)
- Unsafe permission patterns (unbounded file access, unrestricted queries)
- Vague descriptions that give agents no operational boundaries
- Malicious agent skills (reverse shells, credential theft, prompt injection)
- Dangerous capability combinations (data exfiltration flows)
- Insecure agent configurations (no auth, disabled sandbox, open DM policy)
- Skill tampering (rug pull detection via content hashing)
- PII/secret leakage in tool outputs (DLP engine)
- Policy violations at runtime (Runtime Guard)
What it does NOT do:
- Network traffic monitoring
- Container-level sandboxing
- Access control management (it enforces policies, not manages identities)
MIT