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Problem-Aware

202 posts on problem-aware.

AI Security for Customer Support Bots: The Inspection Layer Between the Bot and the Customer Data

Customer support bots reach customer records, payment data, account history, and PII at request time through tool calls and retrieval. The bot reads customer data into the LLM context window, the LLM reasons over it, and the bot acts on the result. The security boundary sits at the HTTP path between the bot and the upstream LLM and tool APIs. This piece walks through the data flows the bot exercises in production, the identity and authorization decisions the inspection layer commits, the audit record the customer auditor and the regulator consume, and the prompt-injection surface that retrieved customer content opens.

customer-support-botai-securityidentity-awareaudit-logsindirect-prompt-injectionpii
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AI Security for RAG Systems: The Inspection Layer Between the Retrieval Output and the Model Call

Retrieval-augmented generation systems read documents from a vector store or a search backend into the model context window before the model reasons. The retrieval step is the point where the system pulls content of varying provenance, authorization, and trustworthiness into the prompt. The security boundary sits at the HTTP path between the retrieval output and the model call. This piece walks through the threat model RAG opens, the identity and authorization decisions the inspection layer commits, the audit record for retrieval-derived content, and the indirect prompt injection surface the retrieved documents expose.

ragai-securityindirect-prompt-injectionidentity-awareaudit-logsretrieval
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Shadow AI Discovery Quiz: A 12-Question Tool to Score Your Organization Against the Six-Week Discovery Framework

Most organizations that decide to address shadow AI start by buying a tool. The tool fires alerts on day one and produces a report nobody can act on. A working discovery program is a sequenced six-week path that begins with what the organization already has and adds inspection only after the surface is mapped. This 12-question quiz scores your organization against each step of the framework and tells you where the next two weeks of work belongs.

shadow-aidiscoveryai-inventoryself-assessmentfree-toolai-governance
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Securing CI Pipelines from AI Agent Supply-Chain Attacks

CI pipelines now run coding agents on every pull request. The agent reads the repo, pulls down third-party packages, asks an LLM to write code, executes the suggestion, and pushes a commit. Each step is an attack surface a 2024-era CI threat model did not contemplate. This piece walks through the supply-chain attacks that already shipped in production CI in 2026, where the control point sits at the AI request boundary, and the per-decision audit record a forensic investigator needs to reconstruct the incident.

ai-agent-supply-chainci-cdai-securityinline-enforcementagentic-aiaudit-logs
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AI Agent Supply Chain Attacks: How the Request Boundary Becomes the Failing Surface

AI agent supply chain attacks compromise the agent at one of three points: the model artifact, the tool the agent calls, or the runtime input the agent processes. The HTTP request boundary between the authenticated agent and the LLM endpoint sits underneath all three failure modes. This piece walks through the attack patterns reported in 2025 and 2026, the architectural defects that enable each one, and the inspection-layer control that closes the runtime side of the supply chain risk.

ai-agent-securitysupply-chainagentic-aiprompt-injectioninline-enforcementai-security
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ChatGPT Prompt Injection: How the Attack Surfaces in Enterprise ChatGPT Deployments

ChatGPT prompt injection attacks reach enterprise deployments through three vectors: the Custom GPT instruction-leak surface, the file-upload indirect injection path, and the connected-tool authorization gap that ChatGPT Enterprise opens through GPT actions. This piece walks through each vector, the failure mode the model alone cannot close, and the request-boundary control that produces a deterministic decision and an audit record EU AI Act Article 12 reviewers will accept.

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How to Prevent Prompt Injection: The Four Control Layers That Hold Up in Production

Prompt injection prevention splits into four control layers: prompt construction discipline, retrieval-time content evaluation, request-boundary policy enforcement, and post-response output checks. The first two are application work. The third sits in the inspection layer at the HTTP path between the application and the model. This piece walks through what each layer can and cannot prevent, and the architectural pattern that produces a defensible posture under EU AI Act Article 12 and OWASP LLM01 review.

prompt-injectionllm-securityai-securityinline-enforcementpolicy-enforcementowasp
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Gemini Prompt Injection: The Workspace Integration Surface and the Inspection Layer Response

Gemini prompt injection reaches enterprise deployments through three surfaces that the consumer discussion rarely covers: the Workspace integration path where Gemini reads Gmail, Drive, and Calendar content into the model context, the Gemini API file and URL inputs, and the Vertex AI authorization gap when Gemini is wired into enterprise tools. This piece walks through each surface, the model defenses that fall short, and the request-boundary controls that produce a defensible audit record.

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Claude Prompt Injection: Where the Constitutional AI Defense Falls Short of Enterprise Policy

Claude prompt injection attacks reach enterprise deployments through Anthropic Computer Use, the Files API indirect injection surface, and the MCP connector authorization gap that the Claude developer platform opens. Constitutional AI reduces compliance with the simpler payloads. The training does not enforce the enterprise policy, the user role, or the data classification rules that apply inside a specific organization. This piece walks through each surface and the inspection-layer controls that produce a defensible posture.

prompt-injectionllm-securityai-securityagentic-aiinline-enforcementaudit
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Prompt Injection Test Cases: The Twelve Patterns Your Red Team Has To Run

Prompt injection test cases for production AI deployments cluster into twelve patterns the red team has to exercise: instruction-override, role-reversal, encoded payloads, indirect injection through retrieved content, tool-output injection, multi-turn persuasion, authority impersonation, output-formatting hijack, translation pivot, long-context dilution, system-prompt extraction, and authorization-bypass. This piece walks through each pattern, the payload structure, the expected inspection-layer verdict, and the audit record the test should produce.

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Prompt Injection Mitigation Techniques: The Eight Controls That Hold Up Under Review

Prompt injection mitigation in production AI deployments splits into eight controls: prompt structure, input classifiers, retrieval-time content evaluation, identity-bound policy enforcement, output classifiers, tool call authorization, conversation-aware state checks, and per-decision audit records. This piece walks through what each control catches, what each one misses, and the architectural layer where each fires. The pattern that holds up under EU AI Act Article 12 and DORA Article 19 review.

prompt-injectionllm-securityai-securityinline-enforcementpolicy-enforcementaudit
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Prompt Injection Attack Examples: Ten Production Payloads and the Request-Boundary Response

Prompt injection attack examples in production AI systems cluster into ten repeatable payload families. Each one targets a specific gap between the application instructions and the model context window. This piece walks through the payload, the failure mode the attacker exploits, and the request-boundary response that produces a deterministic block decision and an audit record an EU AI Act Article 12 or DORA Article 19 reviewer will accept.

prompt-injectionllm-securityai-securityinline-enforcementauditowasp
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