AI & LLM Security
— AI Application Source Code Analysis
Offensive security testing customized for AI Application Source Code Analysis risk profiles. Uncover critical vulnerabilities with our dedicated AI & LLM Security experts.
Free Scoping CallAI Application Source Code Analysis delivery and security model
Source-code review for applications that integrate LLMs, RAG, agents, tool calls, and model providers through an AI security lens.
Focus areas
- Prompt, system message, and tool-call code paths
- Model provider keys and logging behavior
- RAG retrieval and data-boundary controls
- Approval and authorization model for agent actions
Delivery notes
- AI flows are reported across code, prompt, and runtime behavior
- Data leakage and tool-abuse scenarios are proven
- Remediation maps to guardrails, permissions, and logging
Decision matrix
AI Application Source Code Analysis 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 |
|---|---|---|---|
| Prompt, system message, and tool-call code paths | Does Prompt, system message, and tool-call code paths create real risk? | Validated against the relevant code, request, configuration, or runtime behavior in AI & LLM Security. | AI flows are reported across code, prompt, and runtime behavior |
| Model provider keys and logging behavior | Does Model provider keys and logging behavior create real risk? | Validated against the relevant code, request, configuration, or runtime behavior in AI & LLM Security. | Data leakage and tool-abuse scenarios are proven |
| RAG retrieval and data-boundary controls | Does RAG retrieval and data-boundary controls create real risk? | Validated against the relevant code, request, configuration, or runtime behavior in AI & LLM Security. | Remediation maps to guardrails, permissions, and logging |
| Approval and authorization model for agent actions | Does Approval and authorization model for agent actions create real risk? | Validated against the relevant code, request, configuration, or runtime behavior in AI & LLM Security. | AI flows are reported across code, prompt, and runtime behavior |
What if Prompt, system message, and tool-call code paths 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 Model provider keys and logging behavior 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 RAG retrieval and data-boundary controls 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
Asset Recon
Attack surface mapping & asset enumeration
Risk Modeling
Penetration testing beyond automated scanners
Exploit Chaining
PoC validation for every finding
Quality & Reporting
Remediation code + free retest
Frequently Asked Questions
What decision does AI Application Source Code Analysis clarify?
AI Application Source Code Analysis clarifies exploitability, affected workflows, and release impact for AI & LLM Security with evidence rather than scanner noise.
What evidence is included in AI Application Source Code Analysis?
AI flows are reported across code, prompt, and runtime behavior Also, Data leakage and tool-abuse scenarios are proven. 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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