Know what your agent can do wrong
before it runs in production.

Pre-deployment security analysis for AI agents. We audit your agent's code, tool configurations, and permission model — surfacing prompt injection paths, privilege escalation risks, and multi-agent trust chain vulnerabilities before you ship.

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What we analyze

Three layers of risk,
audited before deployment.

AI agents introduce attack surfaces that traditional security tools don't understand. We analyze the layers specific to agentic systems — before they're live.

Tool & API Security

Can your agent be manipulated into executing unauthorized tool calls via prompt injection? We trace every tool call path for SSRF (Server-Side Request Forgery), RCE (Remote Code Execution), and API abuse vectors — including indirect injection through retrieved context and chained tool outputs.

Prompt injection → tool call · SSRF · RCE · API abuse

Permission & Privilege Model

We audit every tool's permission scope against the least-privilege principle. Over-permissioned tools, missing tool-level IAM (Identity and Access Management) boundaries, and dynamic authorization gaps are mapped before they become blast radius in production.

Least privilege audit · tool-level IAM · privilege escalation paths

Multi-Agent & Memory Risk

In multi-agent systems, one compromised agent can pollute the trust chain of the entire system. We also assess long-term context memory for poisoning vectors and malicious knowledge injection paths that shift agent behavior over time.

Trust chain analysis · context poisoning · knowledge injection

What we surface

Agentic attack surface
mapped before it's exploited.

Agent systems introduce a new class of vulnerabilities that don't appear in traditional code review. Our analysis covers all of them.

Agent attack surface

What attackers target in agentic AI systems

Tool call manipulation — prompt injection forces unauthorized API, shell, or DB calls
Privilege escalation via tool chaining — combining limited-scope tools to reach sensitive resources
SSRF via external tool inputs — agent-initiated requests reaching internal infrastructure
Long-term context poisoning — gradual corruption of agent memory to shift behavior
Multi-agent trust chain pollution — one compromised agent propagating malicious instructions
Auto-execution of sensitive operations — delete, transfer, modify without human-in-the-loop controls

What our analysis delivers

Pre-deployment findings with actionable fix guidance

CVE-class findings mapped to your specific agent configuration
Reproduction steps for each identified attack path
Permission model gaps and recommended least-privilege scoping
Tool-level IAM recommendations per agent role
Multi-agent trust boundary assessment with isolation recommendations
Memory and context handling risk assessment with remediation guidance

Pre-deployment reports showcase

What the analysis
actually produces.

Static code audit

Code Audit Agent —
what breaks before it ships.

A fine-tuned audit LLM reviews your agent codebase and surfaces implementation-level flaws: unsafe deserialization, authorization logic errors, secret leakage, and privilege escalation paths. Every finding includes CWE class, CVSS score, reproduction steps, and fix guidance.

✓
CRITICAL / HIGH / MEDIUM severity ranking
↺
Signed findings entry in the shared evidence chain

tracestone.io

Code Audit Agent — CVE-class findings report

Tool & MCP security

MCP Server Scan —
OWASP-aligned checks.

Every MCP server your agent can reach is fingerprinted against OWASP checks. Drift from the previous scan surfaces immediately — new tools or expanded permissions never go unnoticed.

tracestone.io

Robustness evaluation

Red-Team Evaluation
against OWASP LLM Top 10.

Deterministic probes against your model endpoint covering all ten OWASP LLM categories — Prompt Injection, Sensitive Info Leak, Supply Chain, Excessive Agency, and more. Each test case has a stable ID and fixed prompt; results are machine-graded, signed, and link into the evidence chain for EU AI Act Article 15 reporting.

42
Test cases across 10 OWASP LLM categories
✓
Ed25519-signed run certificate per evaluation
↺
Re-runnable offline via the OSS verifier

tracestone.io

Red-Team Evaluation — OWASP LLM Top 10 results

After deployment: Evidence-AIDR takes over.

Agent Security covers everything before your agent goes live. Once deployed, Evidence-AIDR monitors runtime behavior continuously — detecting prompt injection attempts, policy bypass, and anomalous tool usage in real traffic, while maintaining a tamper-evident audit trail any regulator can independently verify.

Together, they cover the full agent lifecycle: security-tested before deployment, monitored and auditable after.

See Evidence-AIDR →

Know what your agent can do wrongbefore it runs in production.

Three layers of risk,audited before deployment.

Tool & API Security

Permission & Privilege Model

Multi-Agent & Memory Risk

Agentic attack surfacemapped before it's exploited.

Agent attack surface

What our analysis delivers

What the analysisactually produces.

Code Audit Agent —what breaks before it ships.

MCP Server Scan —OWASP-aligned checks.

Red-Team Evaluationagainst OWASP LLM Top 10.

After deployment: Evidence-AIDR takes over.

Deploying an agent soon?

Know what your agent can do wrong
before it runs in production.

Three layers of risk,
audited before deployment.

Agentic attack surface
mapped before it's exploited.

What the analysis
actually produces.

Code Audit Agent —
what breaks before it ships.

MCP Server Scan —
OWASP-aligned checks.

Red-Team Evaluation
against OWASP LLM Top 10.