192 posts on problem-aware.
Agentic AI workflows chain LLM calls across tools, data stores, and other agents. Most deployments authenticate the human at the front door and run the rest of the chain on shared service credentials. The audit trail collapses by the second hop.
Generative AI returns a response to a human-issued prompt and waits for the next instruction. Agentic AI issues prompts on its own initiative, applies the response, and chains the next call. The architectural divergence has direct consequences for identity, policy enforcement, and audit trails.
Agentic AI security is the practice of constraining what autonomous agents can request, what data they can include in prompts, and what evidence each decision leaves behind. Static credentials, model guardrails, and application logs fail the test. The enforcement layer has to sit at the HTTP AI request boundary.
LangChain, LangGraph, AutoGen, CrewAI, and the OpenAI Assistants API each ship a different agent loop. The security properties of each framework determine what an enforcement layer can see and what it cannot. The architectural divergence matters at the AI request boundary.
Six agentic AI architecture patterns dominate production deployments today: ReAct, plan-and-execute, multi-agent crews, retrieval-augmented agents, code-executing agents, and tool-using single agents. The security architecture differs across each. The enforcement layer always sits at the HTTP AI request boundary.
Prompt injection is the class of attacks where adversarial content in a prompt overrides the application instructions or extracts data the model was not authorized to reveal. The attack surface includes direct user prompts, indirect injection through retrieved documents and tool results, and chained injection through agent loops. OWASP has consistently ranked prompt injection as the top LLM vulnerability. This piece walks through the attack mechanisms in production, the failure modes of model-side defenses, the request-boundary controls that produce a defensible posture, and the audit record format that holds up after an attempt is detected.
OWASP LLM01 captures both direct and indirect prompt injection in a single category in the 2025 update. The architectural reason is that the control point is the same: the request boundary. Application-side defenses fail by construction because the application cannot tell which spans of the prompt the model treats as instructions. Model-side defenses fail because refusal training is probabilistic. This piece walks through the LLM01 attack surface, the inspection-layer controls that produce a defensible posture, the audit record that survives review under EU AI Act Article 12 and DORA Article 19, and the deployment pattern that fits a production AI stack.
The OWASP LLM Top 10 enumerates the application-security risks that show up when an LLM is wired into a production application. The 2025 update reorganized the list to reflect what production teams actually see: prompt injection at the top, sensitive information disclosure and supply chain risk close behind, and a new category for unbounded resource consumption. This piece walks each risk to the inspection layer control that produces a defensible posture, the gap each risk exposes in standard application-side defenses, and where the audit record series intersects EU AI Act Article 12 and DORA Article 19 evidence obligations.
LLM audit logging implementations split along three architectural patterns: in-application logs, sidecar collectors, and inline inspection layers. The inline pattern is the only one that produces records the EU AI Act Article 12, DORA Article 19, and Fannie Mae LL-2026-04 reviewers accept because it is the only one that satisfies the write-path independence test. This piece walks through the three patterns, the architectural reason the first two fall short, the integration points the inline pattern requires, the field set the records have to carry, and the latency budget that fits a production deployment.
Jailbreaking is the class of attacks where adversarial prompts cause the model to disregard the safety training and produce content the provider intended to suppress. The attack catalog spans role-play framing, multi-step persuasion, encoded payloads, and the fine-tuning bypass that targets the refusal patterns directly. Stanford Trustworthy AI and the AIUC-1 Consortium research found that refusal behaviors degrade significantly under adversarial pressure. This piece walks through the attack patterns in production, why the model alone cannot defend, and the request-boundary controls and audit record format that produce a defensible posture.
Indirect prompt injection is the attack pattern where adversarial content reaches the model through a retrieved document, a tool result, or any other source the model treats as part of its context. The attacker never interacts with the application directly. The injection succeeds when the model executes the embedded instructions on the next retrieval or the next agent loop iteration. RAG pipelines and tool-using agents are exposed by construction. This piece walks through the attack mechanics, the surface area in production deployments, why the model alone cannot defend, and the request-boundary controls that produce a defensible posture.
AI audit logs that survive regulatory review carry a specific set of fields the EU AI Act Article 12, DORA Article 19, Fannie Mae LL-2026-04, NIST AI RMF, and HIPAA all expect on the same record. The fields cover identity, decision provenance, model identity, policy state, and integrity metadata. The format has to support per-record retrieval and per-series replay. The write path has to sit outside the application so the application cannot modify the record. This piece walks through the field-level format specification, the integrity model, the storage characteristics, and the deployment pattern that produces records the regulator and the customer auditor will accept.