Cycode
Overview
Product details compiled from public sources, each with a citation.
Matrix Coverage
Where this product defends, by asset class and NIST CSF function. The Coverage column shows whether each asset is Primary, Secondary, or Adjacent to what the product does. The table omits empty rows and columns.
| Asset class | Identify | Protect | Detect | Coverage | Source |
|---|---|---|---|---|---|
| AI Orchestration Tools | Protect: Not covered | Detect: Not covered | Secondary | 2 | |
| AI-Generated Code | Identify: Not covered | Primary | 1 | ||
| AI Model | Protect: Not covered | Detect: Not covered | Primary | 2 |
Framework Relevance
These frameworks include controls relevant to the asset classes Cycode defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Cycode implements these controls or is certified against them.
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| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | AI Orchestration Tools | Agents as deployed artifacts (orchestration view; see AI Agent Identities row for the principal view); system prompts and templates |
| AI Model | Models; Algorithms (model configuration) | |
| CSA AI Controls Matrix | AI Orchestration Tools | Application and Interface Security; Supply Chain Management |
| AI-Generated Code | Application and Interface Security; Supply Chain Management | |
| AI Model | Model Security; Governance, Risk and Compliance | |
| ISO 42001 | AI Orchestration Tools | A.6 AI system life cycle; A.5 Assessing impacts of AI systems |
| AI-Generated Code | A.6 AI system life cycle | |
| AI Model | A.6 AI system life cycle; A.10 Third-party and customer relationships; A.5 Assessing impacts of AI systems | |
| Google SAIF | AI Orchestration Tools | Secure the AI supply chain; application and pipeline security; agent orchestration controls |
| AI-Generated Code | Secure the AI pipeline; code provenance and supply chain integrity | |
| AI Model | Protect the AI model; ensure model integrity, provenance, and weight security | |
| SANS Critical AI Security Guidelines | AI Orchestration Tools | Secure Agentic Systems and AI Autonomy Controls (defined function scope; execution isolation; API and function-call gating); Limit Model Behavior (focused functionality; access controls outside the model) |
| AI-Generated Code | Model I/O Handling (AI deployment in IDEs: prefer local-only integrations to limit exposure of code, keys, and proprietary data); Governance, Risk, Compliance (regularly test and red-team AI applications before and after deployment) | |
| AI Model | Conventional Security Controls (protect model parameters with least privilege, encryption at rest, runtime obfuscation, and trusted execution environments); Data/Model Engineering Controls (adversarial training; alignment and fine-tuning); AI Supply Chain Management (public-model caution; transfer-attack exposure) | |
| MITRE ATLAS | AI Orchestration Tools | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0016 Obtain Capabilities (malicious plugins) |
| AI-Generated Code | AML.T0010 AI Supply Chain Compromise (hallucinated dependencies and slopsquatting); AML.T0018 Manipulate AI Model (when models embed code-execution backdoors) | |
| AI Model | AML.T0043 Craft Adversarial Data; AML.T0024 Exfiltration via AI Inference API (subtechniques: AML.T0024.001 Invert AI Model and AML.T0024.002 Extract AI Model); AML.T0018 Manipulate AI Model (integrity and backdoor) | |
| OWASP AI Exchange | AI Orchestration Tools | Development-time threats: agent framework supply chain; runtime threats: plugin abuse, prompt injection via tools |
| AI-Generated Code | Development-time threats: insecure code generation, license risk, hallucinated dependencies | |
| AI Model | Development-time and runtime model threats: model inversion, extraction, evasion, poisoning | |
| OWASP LLM Top 10 | AI Orchestration Tools | LLM01 Prompt Injection; LLM05 Improper Output Handling; LLM07 System Prompt Leakage; LLM10 Unbounded Consumption |
| AI-Generated Code | LLM06 Excessive Agency (code execution); insecure or vulnerable code patterns inherited from training data | |
| AI Model | LLM03 Supply Chain; LLM04 Data and Model Poisoning; LLM09 Misinformation | |
| OWASP Agentic Security Top 10 | AI Orchestration Tools | ASI01 Agent Goal Hijack; ASI02 Tool Misuse and Exploitation; ASI05 Unexpected Code Execution (RCE); ASI07 Insecure Inter-Agent Communication; ASI08 Cascading Failures; ASI10 Rogue Agents |
| AI-Generated Code | ASI05 Unexpected Code Execution (RCE); ASI04 Agentic Supply Chain Vulnerabilities (hallucinated dependencies and vibe-coding artifacts) | |
| AI Model | ASI04 Agentic Supply Chain Vulnerabilities (model provenance, weights, and dynamic loading) |
Provenance
Last sourced 2026-06-10.
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Sources
- Cycode ADLC security product page
- “Protect every AI interaction across the ADLC with visibility, governance, and guardrails.”
- Cycode blog on mapping AI tools in the SDLC
- “organizes every detected AI component into a continuously updated, categorized inventory.”
- “Understand exactly how many AI entry points exist in your environment: how many MCPs are active, how many models are invoked”
- Cycode agentic development security platform press release
- Cycode security and trust page
Changelog
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Added to the catalog from the Cycode documentation.
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