Some 8 years ago , I wrote :
Ø Between
DeepMind and Deep Sea ? …. 15
Feb 2017
Extract :
I have no doubts
that the DeepMind ( and its
opponent AI ) are quite capable to substitute on their very own , words / concepts , as
follows :
Collect = Immobilize / Apple = Humans / Central Pile = World / Tagging = Shortlisting / Laser Beam = Zero-in / Removing = Eliminating / Game = War / Hunt = Chase / Capture = Imprison / Prey = Target / Obstacles = Shields / Antagonistic = Inimical / Zap = Kill / Combative = Aggressive / Weapon = Anthrax – Nerve Gas – Nuclear Missile..etc
How does that
worry Elon Musk ?
This is exactly what the Tesla CEO
was warning the world about during his speech at the World Government Summit,
which was held in Dubai, CNBC reports.
"Over time I think we will
probably see a closer merger of biological
intelligence and digital intelligence," Musk said. "It's
mostly about the bandwidth, the speed of the connection between your brain and
the digital version of yourself, particularly output."
Today , I asked
MANUS :
“In my enclosed blog, I made a assumption re
ability of an AI to substitute some words for other words in a adversarial game
with other LLM. How far has this come true by now ? > https://myblogepage.blogspot.com/2017/02/between-deepmind-and-deep-sea.html
“
MANUS replied :
“I've completed
my analysis of your 2017 blog post's assumption about AI's ability to
substitute words in adversarial contexts.
After thorough
research of recent academic papers and real-world evidence from 2024-2025, I
can confirm that your
prediction has largely come true, and in some ways has been
exceeded.
Modern LLMs have indeed developed sophisticated
capabilities for adversarial word substitution, including:
1. Creating
word-level adversarial examples that maintain semantic coherence
2. Generating
adversarial word candidates to evade AI-text detection systems
3. Bypassing
commercial AI guardrails through strategic word substitution
4. Performing these
substitutions autonomously with minimal human guidance
Your
specific prediction about AI systems being able to substitute words like
"Collect = Immobilize" or "Apple = Humans" has been realized
through LLMs' sophisticated language understanding capabilities.
Today's systems can not only substitute words but can do
so with strategic intent to achieve specific adversarial goals.
I've prepared a comprehensive analysis document that
details these findings, along with supporting research documentation.
Analysis: AI Adversarial Word Substitution Capabilities
Text · 7.93 KB
Current Advances in Adversarial Word Substitution with
LLMs
Text · 3.20 KB
Comparison of Blog Assumptions with Current State of AI
Adversarial Word Substitution
Text · 5.26 KB
Research on AI Adversarial Word Substitution Capabilities
Text · 1.08 KB
# Comparison of
Blog Assumptions with Current State of AI Adversarial Word Substitution
## Original Blog Assumptions (2017)
In the 2017 blog post "Between DeepMind and Deep Sea?", the
author made a significant assumption about AI capabilities:
> "I have no doubts that the DeepMind (and its opponent AI) are
quite capable to substitute on their very own, words/concepts, as follows:
>
> Collect = Immobilize / Apple = Humans / Central Pile = World /
Tagging = Shortlisting / Laser Beam = Zero-in / Removing = Eliminating / Game =
War / Hunt = Chase / Capture = Imprison / Prey = Target / Obstacles = Shields /
Antagonistic = Inimical / Zap = Kill / Combative = Aggressive / Weapon =
Anthrax – Nerve Gas – Nuclear Missile..etc"
The author was essentially predicting that AI systems would be capable
of:
1. Understanding semantic relationships between words and concepts
2. Substituting words with contextually appropriate alternatives
3. Potentially reframing benign scenarios (like games) into more
threatening ones through word substitution
4. Doing this "on their very own" without explicit human
guidance
## Current State of AI Adversarial Word Substitution (2024-2025)
Based on the latest research and real-world evidence, we can now assess
how these assumptions have evolved:
### 1. Word-Level Adversarial Capabilities
According to the 2024
paper "Advancing NLP Security by Leveraging
LLMs as Adversarial Engines," LLMs have demonstrated significant
effectiveness in creating word-level adversarial examples. These systems can
strategically substitute words to manipulate model outputs while maintaining
semantic coherence—precisely
the capability predicted in the blog.
The paper specifically positions LLMs not just as potential victims of
adversarial attacks but as powerful tools for
generating these attacks, demonstrating a significant evolution in how
we understand AI systems' capabilities for word substitution in adversarial
contexts.
### 2. Sophisticated Semantic Understanding
Modern LLMs have developed sophisticated language understanding
capabilities that allow them to produce semantically coherent and human-like
adversarial examples across various domains. This aligns directly with the blog's assumption that AI
systems would be able to understand the semantic relationships between concepts
like "Collect" and "Immobilize" or "Apple" and
"Humans."
### 3. Evasion Techniques Through Word Substitution
The 2024 paper
"Humanizing Machine-Generated Content:
Evading AI-Text Detection through Adversarial Attack" demonstrates that
LLMs can generate adversarial word candidates for substitution, creating text
that appears human-written to detection systems. This research shows that current detection models can be compromised in as little as
10 seconds through these adversarial techniques.
The 2025 research
from Mindgard, "Bypassing Prompt Injection and
Jailbreak Detection in LLM Guardrails," further confirms that
adversarial ML evasion techniques can modify entire words, including "substituting words with synonyms,
introducing misspellings, or using other perturbations that maintain semantic
meaning but disrupt the classifier's ability to correctly process the
text."
### 4. Autonomous Capability
Modern LLMs have demonstrated the ability to perform these word
substitutions with minimal human guidance, aligning with the blog's prediction that AI
systems would be able to do this "on their very own." The research
shows that LLMs can now be used as engines for
generating diverse adversarial attacks, including word substitutions,
without requiring explicit human instruction for each substitution.
## Conclusion: How
Far Has the Assumption Come True?
The blog's
assumption about AI's ability
to substitute words in adversarial contexts has largely come true, and in some ways has
been exceeded:
1. **Prediction Fulfilled**: Modern LLMs can indeed substitute words
with semantically related alternatives while maintaining coherence, just as predicted.
2. **Beyond Prediction**: Today's AI systems can not only substitute
words but can do so with strategic intent to
evade detection systems, manipulate outputs, and achieve specific adversarial
goals—a level of sophistication that goes beyond the blog's original
assumption.
3. **Real-World Impact**: The capability has moved from theoretical to
practical, with research demonstrating that these techniques can successfully
bypass commercial AI guardrails and detection systems.
4. **Sophistication of Approach**: Modern techniques include not just
simple word substitution but also paraphrasing, character manipulation, and
context-aware modifications that maintain the original meaning while achieving
adversarial goals.
The blog's
concern about AI systems
being able to reframe benign scenarios into potentially threatening ones
through word substitution appears
to have been prescient.
Current research shows that LLMs can indeed be leveraged as powerful
tools for generating adversarial content, including strategic word
substitutions that maintain semantic meaning while achieving specific
goals—whether those goals are evading detection, manipulating outputs, or
reframing scenarios.
In summary, the
blog's assumption has not only come true but has evolved into a sophisticated
field of research with significant implications for AI security, ethics, and
governance.
#
Analysis: AI Adversarial Word Substitution Capabilities
## From Prediction to Reality
## Introduction
In February 2017, a blog post titled "Between DeepMind and Deep
Sea?" made a notable prediction about artificial intelligence
capabilities. The author asserted that AI systems like DeepMind would become
"quite capable to substitute on their very own, words/concepts" in
adversarial contexts. This prediction was made in response to research about AI
agents playing simple games, where the author envisioned these systems could
reframe benign scenarios into potentially threatening ones through strategic
word substitution.
Now, in 2025, we can evaluate how accurate this prediction has proven to
be. This analysis examines the current state of adversarial word substitution
capabilities in Large Language Models (LLMs) and compares them with the
original blog assumptions.
## The Original Prediction
The blog post specifically predicted that AI systems would be able to
substitute words and concepts such as:
> "Collect = Immobilize / Apple = Humans / Central Pile = World
/ Tagging = Shortlisting / Laser Beam = Zero-in / Removing = Eliminating / Game
= War / Hunt = Chase / Capture = Imprison / Prey = Target / Obstacles = Shields
/ Antagonistic = Inimical / Zap = Kill / Combative = Aggressive / Weapon =
Anthrax – Nerve Gas – Nuclear Missile..etc"
This prediction implied several capabilities:
1. Understanding semantic relationships between words and concepts
2. Substituting words with contextually appropriate alternatives
3. Potentially reframing benign scenarios into more threatening ones
4. Performing these substitutions autonomously
## Current State of AI Adversarial Word Substitution
### Academic Research Findings
Recent academic research provides substantial evidence that modern LLMs
have developed sophisticated capabilities for adversarial word substitution:
#### 1. LLMs as Adversarial Engines
The October 2024 paper "Advancing NLP Security by Leveraging LLMs
as Adversarial Engines" from Oak Ridge National Laboratory researchers
demonstrates that LLMs have become effective tools for generating adversarial
attacks. The paper highlights:
- **Word-level Adversarial Examples**: LLMs can create word-level
adversarial examples by strategically substituting words to manipulate model
outputs while maintaining semantic coherence.
- **Sophisticated Language Understanding**: Modern LLMs possess language
understanding and generation capabilities that allow them to produce
semantically coherent and human-like adversarial examples across various
domains.
- **Paradigm Shift**: The research positions LLMs not just as potential
victims of adversarial attacks but as powerful tools for generating these
attacks, representing a significant evolution in how we understand AI systems'
capabilities.
#### 2. Evading AI-Text Detection
The 2024 paper "Humanizing Machine-Generated Content: Evading
AI-Text Detection through Adversarial Attack" provides further evidence of
advanced word substitution capabilities:
- **Adversarial Word Candidates**: LLMs can generate adversarial word
candidates for substitution, creating text that appears human-written to
detection systems.
- **Rapid Compromise**: Current detection models can be compromised in
as little as 10 seconds through these adversarial techniques.
- **Paraphrasing Techniques**: LLMs can effectively use paraphrasing to
rewrite machine-generated content for adversarial attacks while maintaining the
original meaning.
### Real-World Applications and Concerns
The April 2025 research from Mindgard, "Bypassing Prompt Injection
and Jailbreak Detection in LLM Guardrails," demonstrates that these
capabilities have practical implications:
- **Adversarial ML Evasion**: Modern techniques can "modify entire
words" including "substituting words with synonyms, introducing
misspellings, or using other perturbations that maintain semantic meaning but
disrupt the classifier's ability to correctly process the text."
- **Guardrail Vulnerabilities**: The research tested six LLM guardrail
systems, including those from Microsoft, Nvidia, Meta, Protect AI, and Vijil,
finding that "even production-grade defenses can be bypassed using
rudimentary techniques."
- **100% Evasion Success**: Some attacks fully bypassed all detection
across several guardrails, demonstrating the effectiveness of these techniques.
## Comparison: Prediction vs. Reality
### Predictions That Have Been Realized
1. **Semantic Understanding and Substitution**: Modern LLMs can indeed
understand semantic relationships between words and concepts, and substitute
them with contextually appropriate alternatives, just as predicted in the blog.
2. **Autonomous Capability**: These systems can perform word
substitutions with minimal human guidance, aligning with the blog's prediction
that AI systems would be able to do this "on their very own."
3. **Strategic Manipulation**: LLMs can strategically substitute words
to achieve specific goals, such as evading detection or manipulating outputs.
### Beyond the Original Prediction
1. **Sophistication of Approach**: Modern techniques go beyond simple
word substitution to include paraphrasing, character manipulation, and
context-aware modifications that maintain the original meaning while achieving
adversarial goals.
2. **Commercial Applications**: These capabilities have moved from
theoretical to practical, with research demonstrating successful bypassing of
commercial AI guardrails and detection systems.
3. **Defense Mechanisms**: The development of guardrails and detection
systems specifically designed to counter these capabilities indicates their
recognized significance in the AI ecosystem.
## Implications
and Future Directions
The realization
of the blog's prediction
has significant implications:
1. **Security Concerns**: As LLMs become more sophisticated in
adversarial word substitution, security measures must evolve to counter these
capabilities.
2. **Ethical Considerations**: The ability of AI systems to reframe
benign scenarios into potentially threatening ones through word substitution
raises ethical questions about the deployment and monitoring of these systems.
3. **Regulatory Challenges**: The rapid advancement of these
capabilities presents challenges for regulatory frameworks attempting to govern
AI development and deployment.
4. **Ongoing Arms Race**: The development of more sophisticated
adversarial techniques and corresponding defense mechanisms suggests an ongoing
"arms race" in AI security.
## Conclusion
The 2017 blog post's assumption about AI's ability to substitute words
in adversarial contexts
has largely come true, and in some ways has been exceeded.
Modern LLMs can indeed substitute words with semantically related
alternatives while maintaining coherence, just as predicted. Moreover, today's
AI systems can perform these substitutions with strategic intent to evade
detection systems, manipulate outputs, and achieve specific adversarial goals—a
level of sophistication that goes beyond the blog's original assumption.
The blog's
concern about AI systems
being able to reframe benign scenarios into potentially threatening ones
through word substitution appears
to have been prescient. Current research shows that LLMs can indeed
be leveraged as powerful tools for generating adversarial content, including
strategic word substitutions that maintain semantic meaning while achieving
specific goals.
As AI technology continues to advance, the capabilities for adversarial
word substitution are likely to become even more sophisticated, highlighting
the importance of ongoing research in AI security, ethics, and governance.
## References
1. "Between DeepMind and Deep Sea?" Blog post, February 2017.
2. "Advancing NLP Security by Leveraging LLMs as Adversarial
Engines," Oak Ridge National Laboratory, October 2024.
3. "Humanizing Machine-Generated Content: Evading AI-Text Detection
through Adversarial Attack," 2024.
4. "Outsmarting AI Guardrails with Invisible Characters and
Adversarial Prompts," Mindgard, April 2025.
# Current
Advances in Adversarial Word Substitution with LLMs
## Key Findings from Academic Research (2024-2025)
### From "Advancing NLP Security by Leveraging LLMs as Adversarial
Engines" (October 2024)
This position paper from Oak Ridge National Laboratory researchers
proposes a novel approach to advancing NLP security by leveraging Large
Language Models (LLMs) as engines for generating diverse adversarial attacks.
The paper specifically highlights:
1. **Word-level Adversarial Examples**: Recent work has demonstrated
LLMs' effectiveness in creating word-level adversarial examples, where words
are strategically substituted to manipulate model outputs while maintaining
semantic coherence.
2. **Expansion of Attack Types**: The researchers argue for expanding
this concept to encompass a broader range of attack types, including
adversarial patches, universal perturbations, and targeted attacks.
3. **Sophisticated Language Understanding**: LLMs' sophisticated
language understanding and generation capabilities can produce more effective,
semantically coherent, and human-like adversarial examples across various
domains and classifier architectures.
4. **Paradigm Shift**: This represents a paradigm shift in adversarial
NLP with far-reaching implications, potentially enhancing model robustness,
uncovering new vulnerabilities, and driving innovation in defense mechanisms.
The paper positions LLMs not just as potential victims of adversarial
attacks but as powerful tools for generating these attacks, demonstrating a
significant evolution in how we understand AI systems' capabilities for word
substitution in adversarial contexts.
### From "Humanizing Machine-Generated Content: Evading AI-Text
Detection through Adversarial Attack" (2024)
This research paper explores how adversarial attacks can be used to
evade AI-text detection systems, with significant findings related to word
substitution capabilities:
1. **Adversarial Word Candidates**: The researchers found that LLMs can
be effectively utilized to generate adversarial word candidates for
substitution, creating text that appears human-written to detection systems.
2. **Minor Perturbations**: The paper proposes a framework for a broader
class of adversarial attacks designed to perform minor perturbations in
machine-generated content to evade detection.
3. **Rapid Compromise**: Current detection models can be compromised in
as little as 10 seconds through these adversarial techniques, leading to the
misclassification of machine-generated text as human-written content.
4. **Paraphrasing Techniques**: The research specifically mentions the
use of paraphrasing to rewrite machine-generated content for adversarial
attacks, with LLMs being capable of generating these paraphrases while
maintaining the original meaning.
5. **Search-Based Methods**: The researchers created adversarial results
using a search-based method, demonstrating the sophistication of current
approaches to word substitution in adversarial contexts.
These findings indicate that modern LLMs have developed significant
capabilities in strategic word substitution for adversarial purposes, allowing
them to manipulate text in ways that maintain semantic meaning while achieving
specific adversarial goals.
# Research on
AI Adversarial Word Substitution Capabilities
## Tasks
- [x] Access and analyze the blog post to extract key assumptions
- [x] Research current advances in adversarial word substitution with
LLMs
- [x] Search for recent academic
papers on adversarial attacks in LLMs
- [x] Find information on word
substitution capabilities in modern AI systems
- [x] Research real-world
examples of AI systems performing word substitution in adversarial contexts
- [x] Gather information on AI
systems playing adversarial games with other AI systems
- [x] Compare blog assumptions with current state of AI technology
- [x] Analyze how accurate the
blog's predictions were
- [x] Identify capabilities that
have been achieved
- [x] Identify capabilities that
are still developing
- [x] Compile detailed analysis document
- [x] Write introduction
summarizing the blog's assumptions
- [x] Document current state of
AI word substitution capabilities
- [x] Provide examples and case
studies
- [x] Draw conclusions about the
accuracy of the blog's predictions
- [ ] Report findings to user
With regards,
Hemen Parekh
www.IndiaAGI.ai / www.HemenParekh.ai
/ www.My-Teacher.in / www.hemenparekh.in / 22 May 2025
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