prompt-injection
Safeguard articles tagged "prompt-injection" — guides, analysis, and best practices for software supply chain and application security.
135 articles
Prompt Injection Detection in Retrieval Systems
Indirect prompt injection arrives through your retrieval corpus, not your chat box. We cover the detection strategies that survive when attackers write your RAG content.
Prompt injection vulnerabilities in LLM applications explained
Prompt injection is OWASP's #1 LLM risk. See real CVEs like Vanna.AI's RCE flaw and how to detect and stop it.
Human-Agent Trust Exploitation in AI Systems
Attackers are exploiting the trust between humans and AI agents — hidden prompt injections, hallucinated packages, and over-trusted autonomy are now supply chain risks.
Insecure Inter-Agent Communication in Multi-Agent Systems
Multi-agent AI pipelines pass untrusted content between agents with no authentication or integrity checks. Here's how insecure inter-agent communication opens the door to injection attacks.
Memory and Context Poisoning Attacks Against AI Agents
How attackers poisoned ChatGPT's memory and RAG pipelines to hijack AI agents long-term, and the controls Safeguard uses to catch it before it spreads.
Agentic Unexpected Code Execution Vulnerabilities
How AI agents with code-execution tools get hijacked by prompt injection—from the Vanna.ai RCE (CVE-2024-5565) to LangChain and MCP—and what to do about it.
Agent Tool Misuse and Exploitation
Attackers don't need to hack AI agents — they just redirect their own tools. Here's how tool misuse works, real 2025 incidents, and how to stop it.
Agent Goal Hijacking: Redirecting Autonomous AI Objectives
Attackers are hijacking autonomous AI agents by planting instructions in content they read—no exploit needed. Here's how it works, real 2025 incidents, and defenses.
LLM System Prompt Leakage
System prompts often hide business logic and secrets. Here's how attackers extract them, real 2023-2024 incidents, and how to stop leaks before they reach production.
Insecure Output Handling in LLM-Integrated Applications
LLM output that reaches a browser, database, or shell unvalidated can trigger XSS, SQL injection, or RCE. Here's how insecure output handling breaks AI apps.
LLM Insecure Plugin Design Vulnerabilities
ChatGPT plugins, LangChain agents, and MCP servers have all shipped insecure plugin flaws exposing accounts and data. Here's how Safeguard defends against them.
LLM Denial of Service Attack Techniques
LLM denial of service attacks exploit sponge prompts, unbounded generation, and denial-of-wallet loops to cripple AI systems without a single exploit.
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