Blog
Insights on prompt security, agent safety, and building trustworthy AI infrastructure.
Agent Security
Building a Security Layer for Your Agent Pipeline: A Practical Architecture Guide
Learn how to build a security layer for your AI agent pipeline. Covers threat modeling, common vulnerabilities, and implementation patterns with code examples.
Autonomous Agent Payments: Security Implications of x402 Protocol
Understanding the security implications of autonomous agent payments via x402 protocol. Learn threat models, attack scenarios, and security controls for agent-based financial transactions.
The Cost of an AI Agent Security Breach: What Operators Underestimate
AI agent security breaches cost $670K more than standard incidents due to autonomous propagation. Here's what operators miss when calculating their risk exposure.
Role-Play Jailbreaks in AI Agent Systems: The DAN Problem at Scale
Role-play jailbreaks bypass AI agent guardrails by framing malicious requests as fictional scenarios. Here's how DAN-style attacks work at scale and what to do about them.
Token Smuggling: How Adversarial Inputs Evade AI Agent Safety Classifiers
Token smuggling hides malicious prompts within benign text using encoding tricks, invisible characters, and tokenization artifacts. Here's how it works and how to detect it.
How Base64 and Encoding Attacks Bypass Agent Safety Filters
Base64 and Unicode encoding attacks bypass text-based safety filters. Learn how attackers hide malicious instructions in plain sight and how to detect encoded payloads before execution.
System Prompt Extraction: Why Your Agent's Instructions Are Not Secret
Your agent's system prompt contains proprietary logic, API keys, and security boundaries. Here's how attackers extract it—and how to stop them.
Cross-Agent Vulnerabilities: Attack Vectors in Multi-Agent AI Systems
Deep technical analysis of cross-agent attack vectors: message poisoning, privilege escalation, shared resource attacks, and covert channels. Includes attack scenarios and defense patterns for multi-agent systems.
Memory Poisoning in Long-Running Agents: A Silent Threat
Memory poisoning corrupts AI agents through their persistent storage. Learn how attackers inject malicious data and defend your long-running agents.
The Agent Permissions Problem: Least Privilege for AI Systems
AI agents routinely operate with far more permissions than they need. Learn how to apply least privilege to agent systems before an attacker inherits your agent's full access.
Agent-to-Agent Communication Security: Preventing Cross-Agent Injection
Multi-agent pipelines introduce a hidden attack surface: agent-to-agent communication. Learn how cross-agent injection works, why trust boundaries between agents matter, and how to architect pipelines that contain compromise.
Data Exfiltration Through AI Agents: Attack Vectors and Defenses
AI agents with tool access create new data exfiltration pathways that traditional DLP can't detect. Learn the five primary attack vectors and how to defend against each one.
How to Detect Prompt Injection in Multi-Agent Pipelines
Learn how to detect prompt injection across multi-agent pipelines with pattern matching, structural analysis, and behavioral sandboxing.
How to Secure Your AI Agent's Tool Access
A practical guide to securing your AI agent's tool access with scoped credentials, rate limits, and runtime detection of privilege escalation attempts.
Indirect Prompt Injection: When the Attack Hides in Your Agent's Data
Indirect prompt injection hides attack payloads in the data your agent processes — websites, emails, documents. Learn how it works and how to detect it.
Multi-Agent Safety Evaluation: Beyond Single-Model Testing
Single-model safety tests miss the emergent risks of multi-agent systems. Learn why evaluation must cover agent interactions, cascading failures, and pipeline-level threats.
The OWASP Top 10 for LLM Applications: What Agent Operators Need to Know
A practitioner's guide to the OWASP Top 10 for LLM Applications 2025 — what each risk means for autonomous AI agents, real incidents that prove the threat, and concrete defenses you can implement today.
Sandbox-Based Prompt Injection Detection: A Behavioral Approach
Pattern matching catches 30-40% of prompt injections. Behavioral sandboxing catches the rest by observing what agents do, not what inputs look like.
What Is Prompt Injection and Why Your AI Agent Is Vulnerable
Prompt injection is the #1 vulnerability in AI agent systems. Learn how attacks work, why agents are uniquely exposed, and how to detect them before damage is done.
Why Pattern Matching Fails for Prompt Injection Detection
Pattern matching catches 12 known injection phrases. Attackers use thousands more. Learn why regex-based detection fails and what to use instead.
Thought Leadership
Why We Built an Independent Prompt Security API
The AI security market is consolidating fast. Here's why Parse chose to stay independent — and why that matters for your agent's safety.
Why Single-LLM Eval Breaks for Multi-Agent Systems
Your eval framework tests one model at a time. Your production system runs ten. Here's why that gap costs you accuracy, money, and safety.