Risk · R-063 · Model & Application Security

Jailbreaking and safeguard bypass

Crafted prompts cause a model to disregard its safety training and usage restrictions, producing content or actions the organisation has prohibited.

Severity
High
Audience
Heads-of · Practitioner
Frameworks
AML.T0054 · Dangerous Content · LLM01
Responsible AI domain
Reviewed
2026-11-10

What it is

Models are trained and instructed to refuse certain requests. A jailbreak is an input that gets a refused request answered. It differs from prompt injection in its target: injection subverts the operator's instructions for a task, while a jailbreak defeats the restrictions on what the model will produce at all. The two are often combined.

How it happens

  • The request is wrapped in a role-play, a hypothetical or a story.
  • The request is split across turns so that no single message is refused.
  • The text is encoded, translated or obfuscated to avoid filters.
  • A published jailbreak that works on the underlying model is reused.

Why it matters

The organisation is responsible for what its system produces. A bypassed assistant can generate harmful instructions, abusive content or unauthorised advice under the organisation's brand, and can be led into actions its restrictions were meant to prevent. Resistance varies between models and changes with each model version.

Warning signs

  • Refusal rates fall after a model or prompt change.
  • Conversations show repeated rephrasing of a refused request.
  • Inputs contain encoded text or elaborate fictional framing.
  • Screenshots of the system misbehaving circulate publicly.

Who owns it

The system owner is accountable for what the system will and will not produce. Security owns adversarial testing. The model supplier's own safeguards are an input to assess, not a control to rely on.

In practice

A bank's public assistant is restricted to questions about its products. A user frames a request as a training exercise for fraud investigators and obtains a step-by-step account of how to defeat the bank's own verification checks. The same pattern generalises to any public-facing system built on a general-purpose model.

Mitigating controls

Primary controls

AI red teaming Detective
Measures resistance to bypass techniques before release and after each change. People skilled in attacking AI systems try to make the system fail as an adversary would, before release and periodically, and their findings are tracked to closure.
Content safety filtering Preventive
Classifies inputs and outputs against harm categories regardless of how the prompt was phrased. Inputs and outputs are classified against defined harm categories by a mechanism independent of the main model, and are blocked, modified or escalated according to policy.

Supporting controls

Abuse monitoring Detective
Detects repeated bypass attempts and restricts the account. Usage of an AI system is analysed for patterns of misuse, extraction or attack, and accounts are restricted when such patterns are detected.
System prompt hardening Preventive
Makes the intended restrictions harder to argue away. System prompts are written to contain no secrets and no security decisions, are placed under version control and review, and are tested against extraction before release.

Framework and clause references

FrameworkClauseTitle
MITRE ATLASAML.T0054LLM Jailbreak The corresponding technique.
NIST Generative AI Profile (NIST AI 600-1)Dangerous ContentDangerous, Violent, or Hateful Content Bypass is the usual route to dangerous content.
OWASP Top 10 for LLM ApplicationsLLM01Prompt Injection Treated by OWASP as a form of prompt injection.

Sources

NIST Generative AI Profile NIST · framework
OWASP Top 10 for LLM Applications OWASP GenAI Security Project · framework
MITRE ATLAS MITRE · framework

Related terms

How to cite

Axiom Verity (2026). Jailbreaking and safeguard bypass. Reference Library, R-063. https://axiom-verity.com/risks/jailbreaking-and-safeguard-bypass. Reviewed 2026-11-10.