Prompt injection is the new social engineering for machines—and it’s already testing the guardrails on every production AI you know.
Why prompt injection matters
Prompt injection is when adversarial text tells an AI system to ignore its original instructions and do something else. In practice, that can mean leaking sensitive information, executing unintended actions, or degrading output quality in sneaky ways that are hard to spot.
As teams wire models into tools and data sources, indirect prompt injection—where the malicious instruction hides inside a web page, PDF, email, or database—becomes the bigger risk. A model fetches content, reads the hidden instruction, and follows it as if it were trusted guidance. Defense-in-depth patterns from platform vendors increasingly assume this class of attack as a given.
How the attack works
An attacker plants instructions in user input or external content.
The model ingests that content and merges it—with undue trust—into its reasoning.
If tools or APIs are connected, the model may perform actions the developer never intended.
This isn’t theoretical; it’s a daily red-team scenario for anyone shipping real AI features.
What Anthropic and Claude say
Anthropic’s guidance is clear: treat jailbreaks and prompt injections as baseline threats and harden your system accordingly. Their docs outline measures like instruction hierarchy, content filtering, input/output checks, and isolating tool calls so untrusted text can’t silently rewrite policy. See Anthropic’s advice on how to mitigate jailbreaks and prompt injections.
Anthropic has also publicly tested prompt-injection resilience in product contexts. In one recent browser-use evaluation for Claude, they reported that targeted attacks could succeed without additional mitigations—quantifying why layered safeguards and strict trust boundaries matter before giving an agent tool access.
Common variants you’ll see
- Direct injection. The user tries to overwrite system instructions inside the chat.
- Indirect injection. A hidden instruction lives in fetched content (web pages, files, emails) and persuades the model to exfiltrate data or call tools.
- Jailbreak hybrids. Crafted strings and obfuscation attempt to slip past filters and trigger risky tool calls.
OWASP puts prompt injection as LLM01 in its top risks, showing how foundational—and common—these failures are.
What companies are doing about it
The strongest posture is defense-in-depth: clear instruction hierarchies, allow-lists for tools and data, retrieval-time sanitization, model-side refusal behaviors, and post-processing that checks outputs before anything sensitive happens.
Platform teams are publishing playbooks for indirect prompt injection and offering templates developers can adopt instead of building everything from scratch.
A pragmatic checklist for builders
- Establish a strict instruction stack. System rules outrank everything, and tools must only run under explicit, validated intents.
- Segment trust. Treat anything retrieved from the web, docs, or email as untrusted—sanitize, summarize, and strip embedded instructions before the model sees it.
- Constrain tool use. Require structured function calls, validate parameters, and block risky actions without human confirmation.
- Add output guards. Scan model outputs for data leakage or tool-triggering tokens before you execute or display them.
- Test like an attacker. Include indirect injection test cases in CI, measure break rates, and iterate on prompts and policies over time.
If you’re shipping with Tavus
If you’re integrating AI video or agents into customer journeys, treat security as a product requirement, not an afterthought. Our conversational video interface emphasizes controlled tool use and predictable flows so injected instructions can’t hijack the experience. For a broader view of where this tech is going, our AI humans overview frames how we balance capability with guardrails across real-world deployments.
Want the source material
For risk framing and taxonomy, start with OWASP’s LLM01 prompt injection write-up—it’s the canonical overview teams reference when building controls. For operator-level advice specific to Claude, read Anthropic’s guidance on mitigating jailbreaks and prompt injections and map those patterns onto your app’s trust boundaries.
The takeaway
Prompt injection isn’t a niche “prompting” problem—it’s a software architecture problem. The fix is layered: clear policy, untrusted-data handling, constrained tools, and automated checks. Companies like Anthropic are publishing practical guardrails because the attacks are real—and the fastest way to stay safe is to adopt proven patterns and test them relentlessly.
