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ci(deps): bump actions/checkout from 6 to 7 - #10

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ci(deps): bump actions/checkout from 6 to 7#10
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Bumps actions/checkout from 6 to 7.

Release notes

Sourced from actions/checkout's releases.

v7.0.0

What's Changed

New Contributors

Full Changelog: actions/checkout@v6.0.3...v7.0.0

v6.0.3

What's Changed

New Contributors

Full Changelog: actions/checkout@v6...v6.0.3

v6.0.2

What's Changed

Full Changelog: actions/checkout@v6.0.1...v6.0.2

v6.0.1

What's Changed

Full Changelog: actions/checkout@v6...v6.0.1

Changelog

Sourced from actions/checkout's changelog.

Changelog

v7.0.0

v6.0.3

v6.0.2

v6.0.1

v6.0.0

v5.0.1

v5.0.0

v4.3.1

v4.3.0

v4.2.2

v4.2.1

... (truncated)

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Bumps [actions/checkout](https://github.com/actions/checkout) from 6 to 7.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](actions/checkout@v6...v7)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-version: '7'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

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🤖 AI Surface Check

37 production AI surfaces · 42 risk indicators · 2 high, 1 medium

Detectors: mcp_audit, llm_sdks, agent_frameworks, env_keys, model_gateways, ai_infra, api_endpoints, vector_rag

Diff against the base branch was unavailable; showing full inventory.

Generated by ai-surface v1.0.2. Schema 1.0.
Scan timestamp: 2026-06-22T01:15:01.034628+00:00

Production AI surfaces: 37 · Risk indicators: 42

Severity: 🟠 HIGH 2 · 🟡 MEDIUM 1

LLM SDK Call Sites

Azure OpenAI

Files: src/ai_surface/data/mcp/model_patterns.py, src/ai_surface/detectors/llm_sdks.py

Models: gpt-4o-2024-08-06, GPT-4o, gpt-4o, gpt-4o-mini, gpt-4-turbo, gpt-4, GPT-4, gpt-3.5-turbo, gpt-3.5, GPT-3.5, o1, o1-mini, o1-preview, o3-mini, claude-3-5-sonnet, claude-3.5-sonnet, claude-3-5-haiku, claude-3.5-haiku, claude-3-opus, claude-3-sonnet, claude-3-haiku, claude-2, claude-instant, llama-3.3, llama-3.2, llama-3.1, llama-3, llama3, llama-2, llama2, mistral-large, mistral-medium, mistral-small, gemini-2.0, gemini-1.5-pro, gemini-1.5-flash, gemini-pro, gemini-ultra, command-r-plus, command-r, api.mistral.ai, anthropic.claude-3-5-sonnet-20241022-v2:0, claude-sonnet-4-6, mistral-large-latest

Risk indicators:

  • ⚠️ non-literal data flows into LLM call

Validate this surface →

"AZURE_OPENAI_DEPLOYMENT",

Anthropic SDK

Files: examples/demo-app/src/llm_service.py

Models: claude-3-5-sonnet-20241022

Risk indicators:

  • ⚠️ non-literal data flows into LLM call

Validate this surface →

from anthropic import Anthropic

OpenAI SDK

Files: examples/demo-app/src/llm_service.py, src/ai_surface/detectors/llm_sdks.py

Models: gpt-4-turbo, text-embedding-3-large

Risk indicators:

  • ⚠️ non-literal data flows into LLM call

Validate this surface →

# JS/TS: from "openai" (not @azure/openai)

AWS Bedrock

Files: examples/demo-app/src/support_workflow.py, src/ai_surface/detectors/llm_sdks.py

Models: meta.llama-3-70b, anthropic.claude-3-5-sonnet-20240620-v1:0, us.anthropic.claude-sonnet-4-20250514-v1:0

Risk indicators:

  • ⚠️ non-literal data flows into LLM call

Validate this surface →

# Python: boto3.client("bedrock") or "bedrock-runtime"

Agent Frameworks

AWS Strands Agent: triage_agent (in examples/demo-app/src/support_workflow.py)

Files: examples/demo-app/src/support_workflow.py

Tools/permissions: fetch_customer_profile, search_knowledge_base, escalate_to_human

triage_agent = Agent(

Validate at runtime: Coming soon: agent validation in APIsec →

🟠 HIGH LangChain Agent: support_agent (in examples/demo-app/src/chat_agent.py)

Files: examples/demo-app/src/chat_agent.py

Tools/permissions: lookup_order, refund_payment, cancel_subscription

Risk indicators:

  • ⚠️ financial action exposed
  • ⚠️ high blast-radius combination

Validate this surface →

Audit risk flags:

  • 🟠 HIGH financial-action: Agent can invoke financial tools (refund_payment)
    • OWASP: LLM06
    • Governance: EU AI Act Art. 9
    • Fix: Gate financial tools behind human approval; least-privilege the agent.
  • 🟠 HIGH high-blast-radius: Agent combines broad read access with financial/destructive actions
    • OWASP: LLM06
    • Governance: EU AI Act Art. 9
    • Fix: Split read and write agents; apply least-privilege per agent.
  • 🟠 HIGH no-human-oversight: High-risk action (financial-action, high-blast-radius) runs with no human approval / in-the-loop gate detected
    • OWASP: LLM06, LLM09
    • Governance: EU AI Act Art. 14
    • Fix: Put a human-in-the-loop approval step in front of this action (approval gate, confirmation, or a LangGraph interrupt). If approval is enforced elsewhere, confirm the gate sits on this path.
support_agent = create_react_agent(llm=llm, tools=tools, prompt="...")

Validate at runtime: Coming soon: agent validation in APIsec →

Claude Tools: detect (in src/ai_surface/detectors/agent_frameworks.py)

Files: src/ai_surface/detectors/agent_frameworks.py

Tools/permissions: ...

# Anthropic-shape standalone: tools=[{"name": "..."}] dict literals

Validate at runtime: Coming soon: agent validation in APIsec →

LangChain (used in 1 file)

Files: examples/demo-app/src/knowledge_base.py

Validate at runtime: Coming soon: agent validation in APIsec →

MCP Servers

🟡 MEDIUM MCP Server (in-house): fixtures/generate_demo.py

Files: fixtures/generate_demo.py

Risk indicators:

  • ⚠️ in-house MCP server (custom code, audit recommended)
  • ⚠️ unverified source

Validate this surface →

Audit risk flags:

  • 🟡 MEDIUM unverified-source: MCP is not from a known/verified publisher; its behaviour and security posture are unknown.
    • OWASP: LLM03
    • Governance: ISO 42001 A.10
    • Fix: Review the source before use; prefer official/verified MCPs; run unverified MCPs in isolation.

Trust: unknown

ledger-mcp", category=CATEGORY_MCP_SERVER,         evidence=_ev(["services/ledger/mcp_server.py"], "FastMCP('ledger-mcp')",                      {"tools": ["query_ledger", "post_entry"]}, [22]),

Validate at runtime: Coming soon: MCP runtime validation in APIsec →

🟠 HIGH MCP Server (in-house): examples/demo-app/src/orders_mcp_server.py

Files: examples/demo-app/src/orders_mcp_server.py

Tools/permissions: lookup_order, refund_payment, cancel_order, delete_customer, update_record

Risk indicators:

  • ⚠️ in-house MCP server (custom code, audit recommended)
  • ⚠️ unverified source
  • ⚠️ financial action exposed

Validate this surface →

Audit risk flags:

  • 🟡 MEDIUM unverified-source: MCP is not from a known/verified publisher; its behaviour and security posture are unknown.
    • OWASP: LLM03
    • Governance: ISO 42001 A.10
    • Fix: Review the source before use; prefer official/verified MCPs; run unverified MCPs in isolation.
  • 🟠 HIGH financial-action: MCP exposes financial tools (refund, charge, payout) to the model.
    • OWASP: LLM06
    • Governance: EU AI Act Art. 9
    • Fix: Gate financial tools behind human approval.
  • 🟠 HIGH no-human-oversight: High-risk action (financial-action) runs with no human approval / in-the-loop gate detected
    • OWASP: LLM06, LLM09
    • Governance: EU AI Act Art. 14
    • Fix: Put a human-in-the-loop approval step in front of this action (approval gate, confirmation, or a LangGraph interrupt). If approval is enforced elsewhere, confirm the gate sits on this path.

Trust: unknown

`,   `destructive action exposed`, `database write exposed` """ from __future__ import annotations  from mcp.server.fastmcp import FastMCP   mcp = FastMCP("orders-mcp")   @mcp.tool() def lookup_order(

Validate at runtime: Coming soon: MCP runtime validation in APIsec →

Model Gateways

Model Gateway: LiteLLM

Files: examples/demo-app/litellm.config.yaml, tests/fixtures/gateways/litellm_proxy/config.yaml

Tools/permissions: gpt-4o, claude-3-5-sonnet, llama-3-70b, claude-sonnet-4, gpt-4, bedrock-claude

Risk indicators:

  • ⚠️ multi-model routing layer (production traffic flows through this)

Validate this surface →

model_list:

Model Gateway: Helicone

Files: examples/sample-outputs/report.json, src/ai_surface/detectors/env_keys.py, src/ai_surface/detectors/model_gateways.py, src/ai_surface/utils/walk.py

Risk indicators:

  • ⚠️ multi-model routing layer (production traffic flows through this)

Validate this surface →

# baseline JSON contains literal strings like HELICONE_API_KEY in its

Model Gateway: Portkey

Files: src/ai_surface/detectors/env_keys.py

Risk indicators:

  • ⚠️ multi-model routing layer (production traffic flows through this)

Validate this surface →

(r"PORTKEY_API_KEY", "Portkey"),

Model Gateway: OpenRouter

Files: src/ai_surface/detectors/env_keys.py, src/ai_surface/detectors/model_gateways.py

Risk indicators:

  • ⚠️ multi-model routing layer (production traffic flows through this)

Validate this surface →

(r"OPENROUTER_API_KEY", "OpenRouter"),

Model Gateway: Cloudflare AI Gateway

Files: src/ai_surface/detectors/model_gateways.py

Risk indicators:

  • ⚠️ multi-model routing layer (production traffic flows through this)

Validate this surface →

(``portkey_ai``, ``helicone.ai``, ``gateway.ai.cloudflare.com``,

AI Infrastructure

AI Workload (Helm): vllm (in tests/fixtures/ai_infra/helm_vllm/values.yaml)

Files: tests/fixtures/ai_infra/helm_vllm/values.yaml

Risk indicators:

  • ⚠️ self-hosted LLM runtime (operational responsibility on the team)

Validate this surface →

repository: vllm/vllm-openai

K8s AI Workload: ollama (in tests/fixtures/ai_infra/k8s_ollama/deployment.yaml)

Files: tests/fixtures/ai_infra/k8s_ollama/deployment.yaml

Risk indicators:

  • ⚠️ self-hosted LLM runtime (operational responsibility on the team)

Validate this surface →

image: ollama/ollama:latest

AI Workload (compose): ollama (in tests/fixtures/ai_infra/compose_ollama/docker-compose.yml)

Files: tests/fixtures/ai_infra/compose_ollama/docker-compose.yml

Risk indicators:

  • ⚠️ self-hosted LLM runtime (operational responsibility on the team)

Validate this surface →

image: ollama/ollama:latest

K8s AI Workload: vllm (in examples/demo-app/deploy/vllm-embeddings.yaml)

Files: examples/demo-app/deploy/vllm-embeddings.yaml

Risk indicators:

  • ⚠️ self-hosted LLM runtime (operational responsibility on the team)

Validate this surface →

image: vllm/vllm-openai:latest

Bedrock provisioned throughput: anthropic.claude-3-5-sonnet-20240620-v1:0

Files: tests/fixtures/ai_infra/terraform_bedrock/main.tf

Risk indicators:

  • ⚠️ high-cost AI infrastructure (billing exposure)

Validate this surface →

resource "aws_bedrock_provisioned_model_throughput" "claude_throughput" {

Bedrock provisioned throughput: anthropic.claude-sonnet-4-20250514-v1:0

Files: examples/demo-app/deploy/bedrock.tf

Risk indicators:

  • ⚠️ high-cost AI infrastructure (billing exposure)

Validate this surface →

resource "aws_bedrock_provisioned_model_throughput" "support_claude" {

AI Workload (Dockerfile): vllm (in tests/fixtures/ai_infra/dockerfile_serve/Dockerfile)

Files: tests/fixtures/ai_infra/dockerfile_serve/Dockerfile

Risk indicators:

  • ⚠️ self-hosted LLM runtime (operational responsibility on the team)

Validate this surface →

FROM python:3.12-slim

AI Workload (Dockerfile): vllm (in tests/fixtures/ai_infra/dockerfile_vllm/Dockerfile)

Files: tests/fixtures/ai_infra/dockerfile_vllm/Dockerfile

Risk indicators:

  • ⚠️ self-hosted LLM runtime (operational responsibility on the team)

Validate this surface →

FROM vllm/vllm-openai:latest

AI Provider API Keys (env)

AI Provider API Keys

Files: examples/demo-app/.env.example, tests/fixtures/env_keys/with_export/.envrc, tests/fixtures/env_keys/with_keys/.env, tests/fixtures/env_keys/with_keys/.env.production, tests/fixtures/golden_app/.env

Risk indicators:

  • ⚠️ multiple AI provider keys present
  • ⚠️ observability/tracing key present (production telemetry to third party)

Validate this surface →

OPENAI_API_KEY=<redacted>

API Endpoints

REST API: GET ...

Endpoint: GET ...

Framework: fastapi · Auth: unknown

Files: src/ai_surface/detectors/api_endpoints.py

# FastAPI / Starlette: @app.get("...") / @router.post("...") etc.

Validate at runtime: Onboard this API for outside-in runtime testing in APIsec →

REST API: POST ...

Endpoint: POST ...

Framework: fastapi · Auth: unknown

Files: src/ai_surface/detectors/api_endpoints.py

# FastAPI / Starlette: @app.get("...") / @router.post("...") etc.

Validate at runtime: Onboard this API for outside-in runtime testing in APIsec →

REST API: GET /path

Endpoint: GET /path

Framework: flask · Auth: unknown

Files: src/ai_surface/detectors/api_endpoints.py

# Flask: @app.route("/path", methods=["GET","POST"]) / @bp.route(...)

Validate at runtime: Onboard this API for outside-in runtime testing in APIsec →

REST API: POST /path

Endpoint: POST /path

Framework: flask · Auth: unknown

Files: src/ai_surface/detectors/api_endpoints.py

# Flask: @app.route("/path", methods=["GET","POST"]) / @bp.route(...)

Validate at runtime: Onboard this API for outside-in runtime testing in APIsec →

REST API: * /route/

Endpoint: /route/

Framework: django · Auth: unknown

Files: src/ai_surface/detectors/api_endpoints.py

# Django: path("route/", view) / re_path(r"^route/$", view)

Validate at runtime: Onboard this API for outside-in runtime testing in APIsec →

REST API: GET /health

Endpoint: GET /health

Framework: fastapi · Auth: unknown

Files: examples/demo-app/src/api.py

@app.get("/health")

Validate at runtime: Onboard this API for outside-in runtime testing in APIsec →

REST API: GET /orders/{order_id}

Endpoint: GET /orders/{order_id}

Framework: fastapi · Auth: unknown

Files: examples/demo-app/src/api.py

Risk indicators:

  • ⚠️ object-id in path (BOLA candidate)

Validate this surface →

@app.get("/orders/{order_id}")

Validate at runtime: Onboard this API for outside-in runtime testing in APIsec →

REST API: GET /customers/{customer_id}

Endpoint: GET /customers/{customer_id}

Framework: fastapi · Auth: unknown

Files: examples/demo-app/src/api.py

Risk indicators:

  • ⚠️ object-id in path (BOLA candidate)

Validate this surface →

@app.get("/customers/{customer_id}")

Validate at runtime: Onboard this API for outside-in runtime testing in APIsec →

REST API: PATCH /customers/{customer_id}

Endpoint: PATCH /customers/{customer_id}

Framework: fastapi · Auth: unknown

Files: examples/demo-app/src/api.py

Risk indicators:

  • ⚠️ object-id in path (BOLA candidate)

Validate this surface →

@app.patch("/customers/{customer_id}")

Validate at runtime: Onboard this API for outside-in runtime testing in APIsec →

Vector Store

Vector store: pgvector

Files: examples/demo-app/src/knowledge_base.py, src/ai_surface/detectors/vector_rag.py

Risk indicators:

  • ⚠️ retrieved content reaches the model (retrieval-augmented generation)
  • ⚠️ application data embedded for retrieval
  • ⚠️ ingests external content (RAG poisoning surface)

Validate this surface →

("pgvector", "pgvector", "self-hosted", [

Vector store: Elasticsearch (vector)

Files: src/ai_surface/detectors/vector_rag.py

Risk indicators:

  • ⚠️ retrieved content reaches the model (retrieval-augmented generation)
  • ⚠️ application data embedded for retrieval
  • ⚠️ ingests external content (RAG poisoning surface)

Validate this surface →

# signals (dense_vector / knn / the LangChain store class), so plain

Vector store: Marqo

Files: src/ai_surface/detectors/vector_rag.py

Risk indicators:

  • ⚠️ managed vector store (indexed data and embeddings leave your environment)
  • ⚠️ retrieved content reaches the model (retrieval-augmented generation)
  • ⚠️ application data embedded for retrieval
  • ⚠️ ingests external content (RAG poisoning surface)

Validate this surface →

("marqo", "Marqo", "managed", [

RAG pipeline: LangChain

Files: examples/demo-app/src/knowledge_base.py

Risk indicators:

  • ⚠️ retrieved content reaches the model (retrieval-augmented generation)
  • ⚠️ application data embedded for retrieval
  • ⚠️ ingests external content (RAG poisoning surface)

Validate this surface →

from langchain_postgres.vectorstores import PGVector

Risk Indicator Summary

  • ⚠️ MCP Server (in-house): fixtures/generate_demo.py: in-house MCP server (custom code, audit recommended)
  • ⚠️ MCP Server (in-house): fixtures/generate_demo.py: unverified source
  • ⚠️ MCP Server (in-house): examples/demo-app/src/orders_mcp_server.py: in-house MCP server (custom code, audit recommended)
  • ⚠️ MCP Server (in-house): examples/demo-app/src/orders_mcp_server.py: unverified source
  • ⚠️ MCP Server (in-house): examples/demo-app/src/orders_mcp_server.py: financial action exposed
  • ⚠️ Azure OpenAI: non-literal data flows into LLM call
  • ⚠️ Anthropic SDK: non-literal data flows into LLM call
  • ⚠️ OpenAI SDK: non-literal data flows into LLM call
  • ⚠️ AWS Bedrock: non-literal data flows into LLM call
  • ⚠️ LangChain Agent: support_agent (in examples/demo-app/src/chat_agent.py): financial action exposed
  • ⚠️ LangChain Agent: support_agent (in examples/demo-app/src/chat_agent.py): high blast-radius combination
  • ⚠️ AI Provider API Keys: multiple AI provider keys present
  • ⚠️ AI Provider API Keys: observability/tracing key present (production telemetry to third party)
  • ⚠️ Model Gateway: LiteLLM: multi-model routing layer (production traffic flows through this)
  • ⚠️ Model Gateway: Helicone: multi-model routing layer (production traffic flows through this)
  • ⚠️ Model Gateway: Portkey: multi-model routing layer (production traffic flows through this)
  • ⚠️ Model Gateway: OpenRouter: multi-model routing layer (production traffic flows through this)
  • ⚠️ Model Gateway: Cloudflare AI Gateway: multi-model routing layer (production traffic flows through this)
  • ⚠️ AI Workload (Helm): vllm (in tests/fixtures/ai_infra/helm_vllm/values.yaml): self-hosted LLM runtime (operational responsibility on the team)
  • ⚠️ K8s AI Workload: ollama (in tests/fixtures/ai_infra/k8s_ollama/deployment.yaml): self-hosted LLM runtime (operational responsibility on the team)
  • ⚠️ AI Workload (compose): ollama (in tests/fixtures/ai_infra/compose_ollama/docker-compose.yml): self-hosted LLM runtime (operational responsibility on the team)
  • ⚠️ K8s AI Workload: vllm (in examples/demo-app/deploy/vllm-embeddings.yaml): self-hosted LLM runtime (operational responsibility on the team)
  • ⚠️ Bedrock provisioned throughput: anthropic.claude-3-5-sonnet-20240620-v1:0: high-cost AI infrastructure (billing exposure)
  • ⚠️ Bedrock provisioned throughput: anthropic.claude-sonnet-4-20250514-v1:0: high-cost AI infrastructure (billing exposure)
  • ⚠️ AI Workload (Dockerfile): vllm (in tests/fixtures/ai_infra/dockerfile_serve/Dockerfile): self-hosted LLM runtime (operational responsibility on the team)
  • ⚠️ AI Workload (Dockerfile): vllm (in tests/fixtures/ai_infra/dockerfile_vllm/Dockerfile): self-hosted LLM runtime (operational responsibility on the team)
  • ⚠️ REST API: GET /orders/{order_id}: object-id in path (BOLA candidate)
  • ⚠️ REST API: GET /customers/{customer_id}: object-id in path (BOLA candidate)
  • ⚠️ REST API: PATCH /customers/{customer_id}: object-id in path (BOLA candidate)
  • ⚠️ Vector store: pgvector: retrieved content reaches the model (retrieval-augmented generation)
  • ⚠️ Vector store: pgvector: application data embedded for retrieval
  • ⚠️ Vector store: pgvector: ingests external content (RAG poisoning surface)
  • ⚠️ Vector store: Elasticsearch (vector): retrieved content reaches the model (retrieval-augmented generation)
  • ⚠️ Vector store: Elasticsearch (vector): application data embedded for retrieval
  • ⚠️ Vector store: Elasticsearch (vector): ingests external content (RAG poisoning surface)
  • ⚠️ Vector store: Marqo: managed vector store (indexed data and embeddings leave your environment)
  • ⚠️ Vector store: Marqo: retrieved content reaches the model (retrieval-augmented generation)
  • ⚠️ Vector store: Marqo: application data embedded for retrieval
  • ⚠️ Vector store: Marqo: ingests external content (RAG poisoning surface)
  • ⚠️ RAG pipeline: LangChain: retrieved content reaches the model (retrieval-augmented generation)
  • ⚠️ RAG pipeline: LangChain: application data embedded for retrieval
  • ⚠️ RAG pipeline: LangChain: ingests external content (RAG poisoning surface)

  • For source-level analysis of mcp servers (shell injection, etc.): mcp-audit

Validate which of these surfaces are exploitable in a running application: apisec.ai/products

Validate at runtime in APIsec

Runtime validation routes available from this scan: mcp-runtime, agent-validation, api-runtime


Powered by ai-surface. To validate which of these surfaces are exploitable in a running application: apisec.ai/products.

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