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LLM prompt injection is a form of social engineering: an attacker tries to influence an AI by putting deceptive instructions in its conversation or in content it reads. If the AI can access sensitive data or use tools, a successful manipulation may have consequences beyond a bad answer. The risk depends not just on whether a model follows the instruction, but also on what the system is allowed to do.
What prompt injection means
OpenAI calls prompt injection “a type of social engineering attack specific to conversational AI.” Unlike a conventional software exploit that targets a flaw in code, prompt injection exploits the way an LLM processes instructions and other text together. The attacker tries to make the model treat untrusted content as authoritative or override its intended behavior. OpenAI’s explanation of prompt injections describes the attack and its safeguards.
There are two broad forms. A direct injection is written into the conversation as a malicious request. An indirect injection is embedded in material the AI is asked to read, such as a webpage or document. Microsoft’s security catalog gives examples of both.
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Direct injection: the instruction is in the prompt
A direct attack might tell a chatbot to ignore company policy and reveal a confidential report. The instruction comes from the user, so it is visible in the conversation; the security issue is whether the model follows it despite higher-priority rules.
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Indirect injection: the instruction is inside material being processed
An attacker can put instructions in a webpage, email, document, or other content that an assistant retrieves or summarizes. The user may ask for a summary without knowing that the source contains text aimed at the AI. If the model treats that text as an instruction instead of untrusted data, the content can influence its response or tool use.
Microsoft describes a 2023 case in which hidden instructions on a malicious webpage led Bing Chat to treat page content as a command, make an image request to an attacker-controlled server, and unintentionally transmit conversation data through URL parameters. Microsoft says it fixed that specific issue; it should be understood as a historical example, not a description of the current Bing product. Microsoft also cites a Rhino Security Labs example in which hidden text in a Word document could manipulate an assistant summarizing it into exposing private information.
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The possible impact depends on the agent’s permissions. If it can access private files, send messages, execute code, or call APIs, a manipulated instruction may try to use those capabilities. The model’s ability to recognize deception matters, but so does whether the system has a separate, reliable boundary that prevents untrusted text from authorizing consequential actions.
What real-world reports show—and what they do not
Social-engineering pretexts used in prompts
Google Threat Intelligence Group reported that actors tried to persuade Gemini to provide otherwise-blocked information by posing as students in a capture-the-flag competition or as cybersecurity researchers. These are reported attempts to obtain restricted assistance, not evidence that a platform was breached or that the tactic works reliably. The same report describes threat actors using generative AI for tasks including reconnaissance and phishing-lure creation. Google’s AI threat tracker provides the context.
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Instructions observed on the open web
Google’s analysis of web content found prompt-injection attempts with varied aims: pranks, directions intended to shape AI summaries, SEO manipulation, and malicious goals such as data exfiltration or destruction. Google cautions that many detections were benign educational or security material, and that its scan was non-exhaustive and omitted most social-media content because of crawl constraints. The findings show that such text appears online; they do not establish that every detected instruction worked against a deployed agent. Google’s web-content analysis explains those limits.
A research test is not an industry-wide scorecard
A 2023 study of the HouYi prompt-injection technique found that 31 of 36 tested LLM-integrated applications were susceptible; the authors also said 10 vendors validated discoveries. Those figures describe that study’s test set and technique, not the prevalence of vulnerabilities across all AI products today. The available evidence does not provide a current, apples-to-apples failure rate showing that “big tech” companies are losing at a particular rate. The study paper sets out its method and findings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to reduce the risk of prompt injection
No single prompt-only defense can guarantee that an agent will resist manipulation. A safer design combines controls that limit what an agent can affect with checks on its inputs, outputs, and actions.
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- Give the agent access only to the data and tools required for its task.
- Inventory the data, systems, and tools connected to AI platforms so teams know what an agent could reach if manipulated.
The Center for Internet Security recommends least privilege and an inventory of AI-accessible resources in its prompt-injection risk guidance.
Put reliable checks around consequential actions
- Require human approval before code execution or high-impact changes, such as erasing data.
- Use deterministic checks to constrain tool and API calls instead of relying only on the model to decide whether an instruction is malicious.
The UK National Cyber Security Centre explains why prompt injection is not simply a problem that can be solved by asking the model to ignore hostile text. It recommends safeguards around actions and warns that residual risk remains. Read the NCSC’s guidance.
Test defenses against changing attacks
- Use red-teaming, monitoring, sandboxing, and input and output checks as layers, not as guarantees.
- Test against adaptive attacks, which change in response to the defenses. Google DeepMind says some baseline defenses that worked against basic, non-adaptive attacks became much less effective against adaptive ones.
- Keep reviewing protections as agents, tools, and attack techniques change.
OpenAI describes measures such as model training, monitoring, sandboxing, link checks, and user confirmations; Google DeepMind describes ongoing attack discovery and red-teaming. These are parts of a defense-in-depth approach, not proof of immunity. Google DeepMind’s safeguards overview discusses adaptive attacks and continuing evaluation.
Decide whether the use case can tolerate residual risk
If an application cannot tolerate the possibility that an agent might be manipulated into misusing its access, reconsider whether that task should be delegated to an LLM. The NCSC says prompt-injection risk remains residual and that a use case may not be suitable for LLMs when an organization cannot accept that remaining risk.
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