Questão de Inglês — Interpretação de texto | Reading comprehension — FGV 2025
InglêsInterpretação de texto | Reading comprehension
- Código
- fg124231
- Banca
- FGV
- Órgão
- CGE-SP
- Ano
- 2025
- Nível
- Superior
Read the following text and answer the questions.
Artificial Intelligence: The “lethal trifecta”
LARGE LANGUAGE MODELS (LLMs), a trendy way of building
artificial intelligence, have an inherent security problem: they
cannot separate code from data. As a result, they are at risk of a
type of attack called a prompt injection, in which they are tricked
into following commands they should not. Sometimes the result is
merely embarrassing, as when a customer-help agent is persuaded
to talk like a pirate. On other occasions, it is far more damaging.
The worst effects of this flaw are reserved for those who
create what is known as the “lethal trifecta”. If a company, eager
to offer a powerful AI assistant to its employees, gives an LLM
access to untrusted data, the ability to read valuable secrets and
the ability to communicate with the outside world at the same
time, then trouble is sure to follow. And avoiding this is not just a
matter for AI engineers. Ordinary users, too, need to learn how to
use AI safely, because installing the wrong combination of apps
can generate the trifecta accidentally.
Better AI engineering is, though, the first line of defence. And
that means AI engineers need to start thinking like engineers,
who build things like bridges and therefore know that shoddy
work costs lives.
The great works of Victorian England were erected by
engineers who could not be sure of the properties of the materials
they were using. In particular, whether by incompetence or
malfeasance, the iron of the period was often not up to snuff. As a
consequence, engineers erred on the side of caution, overbuilding
to incorporate redundancy into their creations. The result was a
series of centuries-spanning masterpieces.
AI-security providers do not think like this. Conventional
coding is a deterministic practice. Security vulnerabilities are seen
as errors to be fixed, and when fixed, they go away. AI engineers,
inculcated in this way of thinking from their schooldays, therefore
often act as if problems can be solved just with more training
data and more astute system prompts.
These do, indeed, reduce risk. The cleverest frontier models
are better at spotting and refusing malicious requests than their
older or smaller cousins. But they cannot eliminate risk
altogether. Unlike most software, LLMs are probabilistic. Their
output is driven by random selection from likely responses. A
deterministic approach to safety is thus inadequate. A better way
forward is to copy engineers in the physical world and learn to
work with, rather than against, capricious systems that can never
be guaranteed to function as they should. That means becoming
happier dealing with unpredictability by introducing safety
margins, risk tolerance and error rates.
Overbuilding in the AI age might, for instance, mean using a
more powerful model than is needed for the task at hand, to
reduce the risk it will be tricked into doing something
inappropriate. It might mean imposing limits on the number of
queries LLMs can take from external sources, calibrated to the
risk of damage from a malicious query. And mechanical
engineering emphasises failing safely. If an AI system must have
access to secrets, then avoid handing it the keys to the kingdom.
In the physical world, bridges have weight limits – even if
they are not always stated clearly to drivers. And, importantly,
these are well within the actual tolerances that calculations
suggest a bridge will bear. The time has now come for the virtual
world of AI systems to be similarly equipped.
Adapted from The Economist, September 27th, 2025, p. 10
The metaphor used in avoid handing it the keys to the kingdom (7th paragraph) means avoid giving the system
- Afull control.
- Blimited data.
- Cpointless prompts.
- Dethical boundaries.
- Erestricted permission.