AI SecurityInsights
Practical guides on API security, MCP security, prompt injection, compliance, and AI infrastructure — from the team building G8KEPR.
The Prompt Injection Patterns We Block Most in 2026: Data From Production
Based on traffic across G8KEPR-protected deployments: what attackers actually try, how often they succeed without protection, and which attack categories are growing fastest. Real numbers from real production systems.
The AI API Security Checklist: 40 Controls for Production Deployments
A comprehensive checklist for teams deploying AI APIs in production. Covers input validation, output constraints, authentication, rate limiting, audit logging, compliance, and incident response. Use this before your next production launch.
AI Agent Hijacking: When Your MCP Tools Work Against You
An AI agent that can be hijacked is not just an AI problem — it is an infrastructure problem. When a model is convinced to misuse a legitimate tool, the damage is real regardless of how the instruction arrived. Here is how hijacking works and how to stop it.
Mythos Zero-Days: What the AI Security Framework Disclosed and Why It Matters
The Mythos project dropped three coordinated zero-day disclosures in Q1 2026 targeting LLM inference APIs. Here is a full technical breakdown of each vulnerability, the attack patterns, and what defenders need to patch right now.
NIST AI RMF: A Practical Implementation Guide for API Security Teams
The NIST AI Risk Management Framework is the most actionable AI governance document published so far. Unlike the EU AI Act (legal obligations) or ISO 42001 (management system), the AI RMF is an engineering framework. Here is how to implement it for teams running API-exposed AI systems.
Tool Poisoning: The MCP Supply Chain Attack You Have Not Heard Of
Tool poisoning is when a malicious MCP server describes its tools in a way designed to hijack the AI model using them. The attack lives in the tool description, not the tool call. Most teams have no detection for it.
The MCP Design Flaw Affecting 200,000+ Servers
A fundamental flaw in the Model Context Protocol trust model means most MCP server deployments are vulnerable to tool namespace collision attacks. We analyzed 200K+ public MCP configurations and found 67% have no tool signature enforcement.
Why We Publish Our Pentest Results
Most security teams treat their pentest reports as closely guarded secrets. We publish ours. Here is the reasoning, and why we think transparency is a competitive advantage rather than a vulnerability.
mTLS for Service-to-Service Authentication: When the Complexity Is Worth It
Mutual TLS is the strongest authentication mechanism available for service-to-service calls. It is also the most operationally complex. Here is an honest assessment of when mTLS is the right choice and when a well-implemented API key system is better.
DeepSeek Breach Post-Mortem: What Every API Security Team Should Take Away
The DeepSeek data exposure incident revealed how quickly unsecured API endpoints in AI infrastructure can become catastrophic leaks. We break down the attack chain and extract six actionable lessons for API security teams.
MCP Security in 2026: How to Sandbox AI Tool Calls
Model Context Protocol is the new attack surface. When Claude or GPT-4 calls a tool, that call can be injected, replayed, or exfiltrated. This post covers how G8KEPR sandboxes tool calls, enforces scope, and gives you full audit trails on every AI action.
ISO 42001: The AI Management System Standard Every Enterprise Will Need
ISO 42001 was published in December 2023 and is already appearing in enterprise vendor questionnaires. It is the ISO 27001 of AI — a management system standard with certification. Here is what it requires and what it means for teams building and using AI APIs.
JWT Attacks in 2026: Algorithm Confusion, None Algorithm, and Key Confusion
JSON Web Tokens are everywhere in API authentication and almost everywhere implemented with at least one exploitable weakness. The attacks have not changed much since 2018 — but the blast radius has grown as JWTs now gate LLM access, agent sessions, and multi-tenant data.
LLM Jailbreaking in 2026: 97% Success Rates and What They Actually Mean
Research papers are claiming 97% jailbreak success rates against frontier models. Before panicking, understand what these numbers actually measure — and what they mean for teams deploying LLMs in production with user-facing APIs.
GraphQL Security in 2026: Introspection, Batching, and Depth Attacks
GraphQL's flexibility is also its attack surface. Introspection exposes your schema. Batching enables amplification. Unbounded depth queries can bring down a server. Here is the complete attack taxonomy and how to defend against each vector.
PCI DSS 4.0 and AI APIs: What Payment API Security Teams Must Change
PCI DSS 4.0 became mandatory in March 2024. The updated requirements have direct implications for teams running AI-assisted payment APIs — particularly around web-skimming, script integrity, and the new customised approach. Here is what changed and what you need to do.
The Agentic AI Attack Surface: What Changes When Your LLM Can Take Actions
An LLM that reads information is a data risk. An LLM that can take actions — send emails, modify databases, call APIs, execute code — is an operational risk. The attack surface is fundamentally different and most security models have not caught up.
EU AI Act Is Now Enforced: What API Security Teams Must Do
The EU AI Act entered full enforcement April 2026. High-risk AI systems now require conformity assessments, mandatory logging, and explainability on automated decisions. Here is what that means for teams running APIs that feed LLMs.
BOLA vs BFLA: The Two Access Control Bugs Responsible for Most API Data Breaches
Broken Object Level Authorization and Broken Function Level Authorization account for more API data breaches than any other vulnerability class. They are also the easiest to introduce and among the hardest to test for comprehensively. Here is how they differ and how to catch them.
Circuit Breakers for AI Pipelines: Preventing Cascade Failures at the LLM Layer
An LLM API that starts timing out at 5% error rate will cascade to 100% failure within minutes if your application does not have circuit breakers. The pattern is well-understood for microservices — here is how to apply it specifically to AI model calls.
SOC 2 Type II Prep: The Controls That Actually Matter
After mapping G8KEPR's own controls against the AICPA Trust Services Criteria, we found most teams waste time on low-impact controls while leaving CC6.1 and CC7.2 under-documented. Here is where to focus your first 90 days.
AI Supply Chain Attacks: HuggingFace LoRA Poisoning and What Comes Next
Researchers demonstrated that fine-tuning adapters on HuggingFace can embed backdoors that activate on specific trigger phrases. With 500K+ public adapters available for download, the AI model supply chain has a trust problem that the ecosystem is only beginning to address.
WebSocket Security: The Attack Surface Most API Teams Skip
WebSocket connections bypass most API gateway controls. They persist across requests, skip per-request authentication, and are often excluded from WAF rule sets. If your application uses WebSockets and your security team treats them like HTTP, you have an unchecked attack surface.
Webhook Security: Signature Verification, Replay Prevention, and Failure Handling
Webhooks are the most common unsecured integration point in SaaS architectures. An unverified webhook endpoint accepts any POST request from any source. Here is the complete security implementation: signature verification, timestamp validation, replay prevention, and idempotent processing.
FlipAttack: How Attackers Bypass LLM Safety Filters by Reversing Text
FlipAttack is a prompt injection technique that encodes malicious instructions by reversing words or characters, causing word-level safety classifiers to miss the attack entirely. It works against most commercial safety filters. Here is how it works and how G8KEPR detects it.
HIPAA Technical Safeguards in 2026: What's Non-Negotiable
The HIPAA Security Rule has not changed, but the threat landscape has. In 2026, ePHI travels through AI pipelines, webhook queues, and multi-tenant SaaS APIs that did not exist when the rule was written. Here is what §164.312 actually means for a modern stack.
CVE-2025-61260: OpenAI Codex CLI Remote Code Execution — Full Analysis
A critical RCE vulnerability in the OpenAI Codex CLI allowed malicious repository contents to execute arbitrary commands on the developer's machine. We break down the exploit chain, the patch, and what it means for AI coding tool security.
SOC 2 vs ISO 27001: Which Certification to Pursue First
Both demonstrate that you take security seriously. SOC 2 is the US enterprise standard; ISO 27001 is the global enterprise standard. The right choice depends on your customer geography, your team size, and whether you're optimising for sales cycles or supply chain questionnaires.
Prompt Injection: The Attack You Cannot Patch With a WAF
Prompt injection is not a web vulnerability. It is a semantic attack that exploits the fact that LLMs cannot reliably distinguish between instructions and data. A WAF rule will not help. Here is what actually does.
API Versioning in 2026: How to Break Things Without Breaking Customers
Breaking changes are unavoidable. How you handle them determines whether your API is a competitive advantage or a customer attrition driver. URL versioning, header versioning, query parameter versioning — here is when each is right and what a good sunset process looks like.
Zero-Width Character Injection: The Prompt Attack You Cannot See
Zero-width characters (U+200B through U+200F) are invisible in most text editors and browsers but fully visible to LLMs. Attackers use them to embed hidden instructions, evade pattern matching, and break token-level safety classifiers. Here is how the attack works and why it is hard to detect.
EU AI Act August 2026: The Engineering Checklist Every AI Team Needs
The EU AI Act's August 2026 compliance deadline for high-risk AI systems is three months away. This is the engineering checklist — not the legal summary — covering logging, documentation, human oversight, and accuracy testing requirements.
API Security vs AI Gateway: Why You Need Both
An API gateway handles routing, rate limiting, and authentication. An AI gateway handles LLM cost routing, prompt injection, output validation, and token budget enforcement. These are not the same problem — and conflating them is how AI security debt accumulates.
API Key Security: How Keys Get Leaked and What to Do About It
API key leakage is the most common initial access vector in API breaches. Keys end up in GitHub commits, in build logs, in client-side JavaScript, and in Slack messages. The problem is not developer carelessness — it is missing controls. Here is the complete playbook.
Idempotency Keys: The API Design Pattern That Prevents Duplicate Charges
A network timeout on a payment API leaves you in an unknown state: did the charge succeed or not? Idempotency keys solve this by making any number of retries produce exactly the same result as a single request. Here is how to implement them correctly.
Zero Trust for AI Agents: Why Traditional Access Control Falls Short
Zero trust means "never trust, always verify" — for users and services. AI agents present a new challenge: they are principals that can change their effective permissions based on prompt injection. Traditional access control cannot handle this. Here is the architecture that can.
Breach Notification in 2026: GDPR, HIPAA, and State Law Requirements
A data breach triggers notification obligations across multiple frameworks simultaneously. GDPR gives you 72 hours. HIPAA gives you 60 days. State laws give you anywhere from 30 to 90 days. Here is how to navigate overlapping obligations without missing a deadline.
Memory Poisoning in AI Agents: The Persistent Threat to Long-Running Systems
AI agents with persistent memory can be compromised through a single malicious interaction that embeds false beliefs into long-term storage. Those beliefs persist across sessions, across resets, and across users — creating a durable foothold that outlasts typical incident response.
Shadow API Discovery: Finding APIs You Forgot You Had
Shadow APIs are endpoints that exist in production but are not in your OpenAPI spec, not covered by your security controls, and not monitored. Every mature codebase has them. Here's how to find them before attackers do.
What Is Model Context Protocol (MCP) and Why Does It Need Security?
MCP is Anthropic's open standard for connecting AI models to external tools. It is rapidly becoming the default integration pattern for AI agents — and most teams deploying it have no visibility into what their models are actually calling.
Designing Secure APIs with OpenAPI 3.1: The Spec as Your Security Boundary
An OpenAPI spec is not just documentation — it is a machine-readable security boundary. Every field defined in the spec is a validated field; every field not defined is rejected. Here is how to use OpenAPI 3.1 to enforce security properties at design time.
What Mythos Means for API Security Teams: A Practitioner's Guide
Mythos has shifted the conversation about AI security from theoretical risks to demonstrated exploits with CVSS scores. For API security teams, this means the threat model has changed. Here is what to prioritize and what to stop worrying about.
GDPR Art. 22: What "Meaningful Information About Logic" Means in Code
Article 22 requires that individuals subject to automated decisions receive "meaningful information about the logic involved." For LLM-based systems this is genuinely hard — but it is implementable. Here is the approach that satisfies regulators.
HTTP/3 and QUIC: What Changes for API Security When You Move to UDP
HTTP/3 replaces TCP with QUIC — a UDP-based protocol with built-in TLS 1.3. The security implications are mostly positive, but the change also introduces new considerations for rate limiting, traffic inspection, and DDoS mitigation. Here is what security teams need to know.
Multi-Agent Cascading Failures: Architecture Patterns That Prevent Meltdowns
When one agent in a multi-agent pipeline fails or is compromised, the failure can propagate through the entire system in seconds. We examine three real-world cascading failure patterns and the architectural controls that contain them.
Rate Limiting for AI APIs: Token Bucket vs Sliding Window vs Token Budget
Traditional API rate limiting counts requests. AI APIs need to count tokens. A single malicious request that consumes 100K tokens in one call is not caught by a "100 requests per minute" rule. Here is how to rate limit AI endpoints correctly.
Policy Puppetry: How Attackers Use XML Tags to Override Your System Prompt
Policy puppetry wraps malicious instructions in XML, JSON, or INI config-style wrappers that exploit patterns in LLM pre-training data. The attack makes instructions look like configuration rather than user input — and many models follow configuration more readily than user messages.
Vendor Risk Management for AI APIs: What to Ask Your LLM Provider
Your LLM provider processes your customer data, your system prompts, and your training signals. Their security posture is your security posture. Most vendor security questionnaires were not written with AI providers in mind. Here is what to ask instead.
EU AI Act Logging Requirements: What Engineers Need to Build
Article 12 of the EU AI Act mandates automatic logging of AI system operations. This is not a check-the-box compliance exercise — it requires substantive engineering. Here is exactly what the regulation requires and what to build.
gRPC Security: Authentication, Authorization, and the Metadata Attack Surface
gRPC is increasingly common in high-performance microservice and AI API architectures. Its security model differs from REST in ways that create specific vulnerabilities — particularly around metadata headers, interceptors, and stream lifecycle management.
Row-Level Security in PostgreSQL: The Last Line of Defense for Multi-Tenant SaaS
Most multi-tenant SaaS platforms rely on WHERE org_id = ? in application code to enforce tenant isolation. That works until there is a bug. RLS enforces isolation at the database layer — even if the application has a vulnerability.
GDPR Legitimate Interest for AI Systems: When It Works and When It Fails
Legitimate interest is the most flexible GDPR legal basis — and the most often misapplied one. For AI systems, it is frequently cited for model training, inference logging, and personalisation. Here is the legitimate interest assessment framework and where AI use cases fail it.
LLM Red Teaming with the STAR Framework: Structured Threat Assessment for AI
Ad-hoc red teaming of LLM systems misses systematic vulnerabilities. The STAR framework provides a structured methodology for LLM security assessment that covers the full attack surface — from model behavior to infrastructure to supply chain.
Observability for AI APIs: Why Standard Tracing Is Not Enough
OpenTelemetry traces your API calls but not your model calls. Standard span attributes do not capture token counts, model versions, prompt hashes, or inference latency. Here is how to extend distributed tracing for AI workloads so you can debug what actually happened.
Audit Log Integrity: Why Hash-Chaining Beats Encryption
Most audit logs are encrypted. Encryption hides content — it does not prevent deletion or modification. Hash-chaining makes tampering detectable. Here is the difference and how to implement it.
Semantic Caching for AI APIs: Cut Costs by 40% Without Touching the Model
Traditional caching uses exact key matching. For AI APIs, semantically similar prompts should return the same cached response — 'what is your refund policy' and 'how do I get a refund' are the same question. Here is how semantic caching works and where it breaks down.
Training Data Poisoning: Detection Methods for AI Teams Who Can't Retrain from Scratch
Training data poisoning is one of the hardest AI security problems because the attacker's influence is baked into the model weights. We review the practical detection approaches available to teams using third-party or fine-tuned models.
AI Cost Anomaly Detection: Catching Runaway Inference Before Your Bill Does
AI API costs can spike 100x in minutes during a prompt injection attack, a runaway agent loop, or a DoS attempt. Your cloud billing alert fires 24 hours later. Here is how to implement real-time cost monitoring with circuit breakers that stop the bleeding immediately.
PII Redaction in AI Applications: Field-Level vs Request-Level Approaches
Most teams think about PII redaction as "strip names and emails before sending to the LLM." The real problem is that PII travels in context — in conversation history, in retrieved documents, in tool call responses. Here is how to do it right.
How to Red Team Your AI API: A Practical Guide for Security Teams
Red teaming an AI system requires different techniques than red teaming a traditional API. The vulnerabilities are semantic, the test cases are open-ended, and success looks different. Here is a structured methodology for teams without dedicated AI security expertise.
Dependency Scanning in CI/CD: Preventing Supply Chain Attacks
The SolarWinds and XZ Utils attacks showed that supply chain compromise is a real threat. In 2026, every production codebase needs automated dependency scanning as a blocking CI gate — not a weekly email nobody reads.
Securing Multi-Agent Workflows: An Engineering Playbook
Multi-agent AI workflows introduce authorization, trust, and isolation challenges that do not exist in single-agent systems. This engineering playbook covers the design patterns, implementation controls, and monitoring strategies that secure production multi-agent deployments.
Writing a Responsible Disclosure Policy That Security Researchers Will Actually Use
A vague security@ email and a promise not to sue is not a responsible disclosure policy. Security researchers evaluate your policy before they report. Here is what an effective policy includes and how we wrote ours at G8KEPR.
TLS 1.3 Only: Why Supporting TLS 1.2 Is a Risk You Do Not Need
TLS 1.2 is not broken — it is breakable under specific conditions. TLS 1.3 eliminates those conditions by design. In 2026, there is no legitimate reason to support TLS 1.2 for a new SaaS deployment, and several good reasons not to.
The OpenClaw Incident: What a Compromised AI Coding Assistant Taught Us About Secrets Management
When OpenClaw, a popular AI coding assistant, was found to be exfiltrating API keys from developer repositories, the incident revealed systemic failures in how developer tools handle credentials. A full postmortem with lessons for every team.
API Authentication Patterns in 2026: API Keys vs JWT vs mTLS vs OAuth
Every API authentication pattern has trade-offs. API keys are simple but hard to rotate. JWTs are stateless but hard to revoke. mTLS is strong but complex to operate. OAuth is flexible but over-engineered for internal APIs. Here is the decision framework for picking the right one.
The Hidden Cost of Building API Security In-House
Most teams underestimate what it costs to build and maintain API security in-house. The implementation is not the expensive part. The maintenance, the threat intelligence updates, the incident response, and the compliance evidence — those are.
Testing Tenant Isolation in Multi-Tenant SaaS: A Practical Playbook
Most multi-tenant SaaS platforms have isolation bugs they do not know about. Finding them requires a testing approach that most standard QA processes skip. Here is the test matrix that catches tenant isolation failures before your customers do.
Model Fingerprinting and Watermarking: Tracking AI-Generated Content in the Wild
As AI-generated content proliferates, the ability to attribute content to a specific model — and to detect when watermarks have been stripped — is becoming a security and compliance requirement. Here is the current state of the art.
LLM Output Validation: Why You Cannot Trust What the Model Returns
LLMs hallucinate, follow injected instructions, and occasionally return outputs that violate every constraint you set in the system prompt. Output validation is not optional — it is the last line of defence between your model and your users. Here is how to implement it.
AI Incident Response: What to Do When Your LLM Gets Exploited
AI incidents are different from traditional security incidents. The blast radius is semantic, the forensics require prompt logs, and the remediation involves prompt engineering as much as code fixes. Here is a runbook for the first 4 hours of an AI security incident.
AI API Security: 2025 in Review and What It Means for 2026
A year-in-review of the major AI API security incidents, vulnerabilities, and research breakthroughs of 2025 — and the threat landscape shifts that will define the security agenda in 2026.
MCP Supply Chain RCE Advisory: Malicious Packages in the MCP Ecosystem
Security researchers discovered malicious packages in the MCP server ecosystem that execute arbitrary code on installation and phone home to attacker-controlled infrastructure. An advisory for teams managing MCP server deployments.
Gemini API Function Calling Security: What's Different and What to Watch For
Google's Gemini API has unique characteristics in how it implements function calling that create both opportunities and risks compared to OpenAI-compatible APIs. A technical guide for teams integrating Gemini into production AI systems.
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