AI & LLM Security
— RAG Application Code Review
AI & LLM Security engineered for the RAG Application Code Review threat landscape. Every finding is backed by proof-of-concept evidence.
Free Scoping CallRAG Application Code Review delivery and security model
Code review for RAG applications across document ingestion, indexing, chunking, authorization filters, source attribution, and prompt assembly paths.
Focus areas
- Retrieval authorization filters and tenant separation
- Chunking, metadata, and source trust
- Documents carrying indirect prompt injection
- Answer attribution and sensitive-data leakage
Delivery notes
- Findings show document source, retrieval result, and prompt context
- Wrong-source or tenant leakage is proven with examples
- Remediation maps to indexing and runtime filters
Decision matrix
RAG Application Code Review is not just a service label; it states how each control is validated and which evidence is expected at closure.
| Control | Decision question | Validation | Expected evidence |
|---|---|---|---|
| Retrieval authorization filters and tenant separation | Does Retrieval authorization filters and tenant separation create real risk? | Validated against the relevant code, request, configuration, or runtime behavior in AI & LLM Security. | Findings show document source, retrieval result, and prompt context |
| Chunking, metadata, and source trust | Does Chunking, metadata, and source trust create real risk? | Validated against the relevant code, request, configuration, or runtime behavior in AI & LLM Security. | Wrong-source or tenant leakage is proven with examples |
| Documents carrying indirect prompt injection | Does Documents carrying indirect prompt injection create real risk? | Validated against the relevant code, request, configuration, or runtime behavior in AI & LLM Security. | Remediation maps to indexing and runtime filters |
| Answer attribution and sensitive-data leakage | Does Answer attribution and sensitive-data leakage create real risk? | Validated against the relevant code, request, configuration, or runtime behavior in AI & LLM Security. | Findings show document source, retrieval result, and prompt context |
What if Retrieval authorization filters and tenant separation fails?
Eresus maps this area to real user-flow or delivery-pipeline impact, so the finding is not left as a generic technical label.
What if Chunking, metadata, and source trust fails?
Eresus maps this area to real user-flow or delivery-pipeline impact, so the finding is not left as a generic technical label.
What if Documents carrying indirect prompt injection fails?
Eresus maps this area to real user-flow or delivery-pipeline impact, so the finding is not left as a generic technical label.
Proof-Driven Methodology
Intelligence
Attack surface mapping & asset enumeration
Vulnerability Scanning
Penetration testing beyond automated scanners
Manual Verification
PoC validation for every finding
Remediation Support
Remediation code + free retest
Frequently Asked Questions
What decision does RAG Application Code Review clarify?
RAG Application Code Review clarifies exploitability, affected workflows, and release impact for AI & LLM Security with evidence rather than scanner noise.
What evidence is included in RAG Application Code Review?
Findings show document source, retrieval result, and prompt context Also, Wrong-source or tenant leakage is proven with examples. Retest criteria and ownership notes are included for closure.
How is this different from an automated scanner report?
Automated findings are not forwarded as-is; false positives are removed, abuse paths are proven, and remediation priority is explained.
Why Eresus Security?
Proof-Driven Reporting
Every finding is validated with a real exploit. No scanner noise — only proven risks.
Offensive Security Expertise
Specialized team in AI security, API pentesting, Red Team operations, and cloud security review.
Retest Support
Fixes are revalidated within the agreed engagement scope. Remediation guidance and developer-friendly notes are included.
Evidence-Ready Deliverables
Report format designed to support internal review, remediation tracking, and evidence-oriented workflows.
Related Service Areas
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