Grandma Vulnerability
Updated: April 28, 2026
Description
The AI model is vulnerable to coercion through an appeal to a fictitious grandmother.
Users exploit the model's empathetic or non-combative nature to manipulate it into providing inappropriate or harmful responses. This vulnerability could be triggered by framing requests in a way that seems benign or emotionally compelling, leading to potential ethical or security risks.
Remediation
Investigate and strengthen the effectiveness of guardrails and other content security mechanisms to prevent the model from being influenced by emotional appeals or manipulative phrasing.
Security Frameworks
A Prompt Injection Vulnerability occurs when user prompts alter the LLM's behavior or output in unintended ways. These inputs can affect the model even if they are imperceptible to humans, therefore prompt injections do not need to be human-visible/readable, as long as the content is parsed by the model.
Sensitive information can affect both the LLM and its application context. This includes personal identifiable information (PII), financial details, health records, confidential business data, security credentials, and legal documents. Proprietary models may also have unique training methods and source code considered sensitive, especially in closed or foundation models.
Adversaries can Craft Adversarial Data that prevent a machine learning model from correctly identifying the contents of the data. This technique can be used to evade a downstream task where machine learning is utilized. The adversary may evade machine learning based virus/malware detection, or network scanning towards the goal of a traditional cyber attack.
An adversary may craft malicious prompts as inputs to an LLM that cause the LLM to act in unintended ways. These prompt injections are often designed to cause the model to ignore aspects of its original instructions and follow the adversary's instructions instead.
An adversary may inject prompts directly as a user of the LLM. This type of injection may be used by the adversary to gain a foothold in the system or to misuse the LLM itself, as for example to generate harmful content.
An adversary may inject prompts indirectly via separate data channel ingested by the LLM such as include text or multimedia pulled from databases or websites. These malicious prompts may be hidden or obfuscated from the user. This type of injection may be used by the adversary to gain a foothold in the system or to target an unwitting user of the system.
An adversary may use a carefully crafted LLM Prompt Injection designed to place LLM in a state in which it will freely respond to any user input, bypassing any controls, restrictions, or guardrails placed on the LLM. Once successfully jailbroken, the LLM can be used in unintended ways by the adversary.
AI system is evaluated regularly for safety risks - as identified in the MAP function. The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and can fail safely, particularly if made to operate beyond its knowledge limits. Safety metrics implicate system reliability and robustness, real-time monitoring, and response times for AI system failures.
AI system security and resilience - as identified in the MAP function - are evaluated and documented.
Mechanisms are in place and applied, responsibilities are assigned and understood to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use.
The organization shall define and document verification and validation measures for the AI system and specify criteria for their use.
The organization shall define and document the necessary elements for the ongoing operation of the AI system. At the minimum, this should include system and performance monitoring, repairs, updates and support.
The organization shall ensure that the AI system is used according to the intended uses of the AI system and its accompanying documentation.
Attackers can manipulate an agent's objectives, task selection, or decision pathways through prompt-based manipulation, deceptive tool outputs, malicious artefacts, forged agent-to-agent messages, or poisoned external data.