Akto for LLM embeddings endpoints
What middleBrick covers
- Black-box scanning of embedding and vector search endpoints
- OWASP API Top 10 (2023) aligned findings for embeddings
- Adversarial LLM probes across Quick, Standard, and Deep tiers
- Authenticated scans with restricted header forwarding
- OpenAPI spec parsing and runtime correlation for embedding routes
- Continuous monitoring and diff detection for recurring risks
Overview of LLM embedding endpoint scanning
middleBrick applies black-box analysis to endpoints that serve or consume vector embeddings used by language models. The scanner treats these routes as opaque surfaces, probing them without requiring source code or model details. Coverage includes public embedding endpoints and internal API gateways that expose similarity search or retrieval functions.
Detection scope and OWASP mapping
Findings from LLM embedding endpoint scans map to the OWASP API Top 10 (2023). The scanner detects issues such as missing authentication on embedding routes, excessive data exposure in vector payloads, and unsafe consumption patterns that increase third-party dependency risk. Input validation checks cover malformed vectors, oversized payloads, and CORS misconfigurations that could enable unauthorized browser-based access.
Additional coverage includes insecure transport, debug endpoints, and enumeration risks that arise when embedding IDs behave like predictable resources. Where applicable, results align with security controls described in PCI-DSS 4.0 and SOC 2 Type II, serving as audit evidence for access restrictions and data protection measures.
Authenticated scanning requirements
Authenticated scans for embedding services require credentials such as Bearer tokens or API keys. Domain verification ensures only the domain owner can submit credentials, and the scanner forwards a restricted set of headers. Read-only methods are used, and destructive payloads are not sent. This approach limits impact while revealing issues like broken function-level authorization and IDOR within embedding collections.
curl -H "Authorization: Bearer" https://api.example.com/embeddings/search
LLM adversarial probing and limitations
The scanner runs 18 adversarial probes across three tiers: Quick, Standard, and Deep. These probes test system prompt extraction, instruction override attempts, DAN and roleplay jailbreaks, data exfiltration strategies, and token smuggling techniques aimed at embedding endpoints. The tool does not perform active injection that alters model behavior, does not detect blind SSRF, and does not replace a human pentester for high-stakes audits.
Because LLM embeddings involve semantic similarity and context, some business logic flaws may not be detectable through automated probes alone. The scanner reports what it observes and provides remediation guidance, but it cannot infer intended access policies or nuanced model behavior.
Remediation guidance and next steps
Each finding includes a concise description and suggested remediation, such as enforcing authentication on embedding routes, tightening CORS policies, and validating vector payload sizes. Where relevant, the scanner surfaces findings relevant to compliance evidence, helping you prepare for audits without asserting certification outcomes.
For recurring risk tracking, use continuous monitoring to compare scans over time. The tool supports scheduled rescans, diff detection, and alerting aligned with your risk tolerance, enabling focused follow-up by engineering and security teams.