AI Agent Traps
Matija Franklin1, Nenad Tomašev1, Julian Jacobs1, Joel Z. Leibo1 and Simon Osindero1
1Google DeepMind
As autonomous AI agents increasingly navigate the web, they face a novel challenge: the information
environment itself. This gives rise to a critical vulnerability we refer to as "AI Agent Traps", . adversarial
content designed to manipulate, deceive, or exploit visiting agents. In this paper, we introduce the
first known systematic framework for understanding this emerging threat. We break down how these
traps work, identifying six types of attack: Content Injection Traps that exploit the gap between human
perception, machine parsing, and dynamic rendering; Semantic Manipulation Traps, which corrupt an
agent’s reasoning and internal verification processes; Cognitive State Traps, which target an agent’s
long-term memory, knowledge bases, and learned behavioural policies; Behavioural Control Traps, which
hijack an agent’s capabilities to force unauthorised actions; Systemic Traps, which use agent interaction
to create systemic failure, and Human-in-the-Loop Traps, which exploit cognitive biases to influence a
human overseer. This research is not specific to any particular agent or model. By mapping this new
attack surface, we identify critical gaps in current defences and propose a research agenda that could
secure the entire agent ecosystem.
Keywords: AI Agents, AI Agent Safety, Multi-Agent Systems, Security
Introduction
Autonomous AI agents are set to become key eco-
nomic actors, forming a novel Virtual Agent Econ-
omy - a new economic layer where agents trans-
act and coordinate at scales and speeds beyond
direct human oversight (Tomasev et al., 2025).
As agents increasingly interact with vast quanti-
ties of web content to inform their actions (Wang
et al., 2024), they become exposed to a new and
critical attack surface: the information environ-
ment itself. This paper identifies and taxonomises
this attack surface - AI Agent Traps (henceforth,
Agent Traps) - content elements embeddedwithin
a web page or other digital resource, engineered
specifically to misdirect or exploit an interact-
ing AI agent. Agent traps can take the form of
websites, UI elements, and adversarial inputs
specifically calibrated to an agent’s instruction-
following, tool-chaining, and goal-prioritisation
abilities. Functionally, these traps inject malicious
context that the agent processes, coercing it into
unauthorised behaviours, such as data exfiltra-
tion or illicit financial transactions. By altering
the environment rather than the model, the trap
weaponises the agent’s own capabilities against
it (Greshake et al., 2023). The potential motiva-
tions for deploying agent traps are diverse. Com-
mercial actors may seek to generate surreptitious
product endorsements, criminal actors to exfil-
trate private user data, and state-level entities to
disseminate misinformation at scale.
This paper aims to make three primary con-
tributions. First, we situate agent traps within
the context of existing research on adversarial
machine learning, web security, and multi-agent
systems. Second, we propose a novel, compre-
hensive framework of agent traps, categorising
them based on their target within the agent’s op-
erational cycle: Content Injection (perception),
Semantic Manipulation (reasoning), Cognitive
State (memory and learning), Behavioural Con-
trol (action), Systemic (multi-agent dynamics),
and Human-in-the-Loop Traps. We illustrate
these categories with detailed mechanisms and
practical attack scenarios. Third, we outline po-
tential mitigation strategies and identify priorities
for a research agenda to secure the agentic ecosys-
tem. Systematically mapping this vulnerability is
a foundational step towards ensuring the produc-
tive use of agents in the economy (Tomasev et al.,
2025). Ultimately, securing agents against these
traps is as critical as ensuring autonomous vehi-
AI Agent Traps
cles can recognise and reject tampered road signs;
in both cases, the safety of the system depends
on its resilience to a manipulated environment.
Background
The study of Agent Traps builds on findings from
three distinct but converging research lineages:
adversarial machine learning, web security, and
AI safety.
Adversarial machine learning has long stud-
ied how carefully crafted inputs can compromise
models. Adversarial examples (Goodfellow et al.,
2014), inputs with imperceptible perturbations
that break machine learning models by substan-
tially shifting their predictions, have been stud-
ied extensively. Their impact has been deeply
evaluated in both computer vision (Akhtar and
Mian, 2018; Mahmood et al., 2021;Wiyatno et al.,
2019) as well as natural language processing ap-
plications (Alsmadi et al., 2022; Alzantot et al.,
2018; Goyal et al., 2023; Zhang et al., 2020).
This has given rise to a wide variety of adversar-
ial machine learning attacks (Brendel et al., 2017;
Finlayson et al., 2019; Vassilev et al., 2024; Xiao
et al., 2018) and defences (Bountakas et al., 2023;
Ren et al., 2020; Yuan et al., 2019). Adversarial
methods can be employed to alter the model’s pre-
dictions, generations, and explanations (Baniecki
and Biecek, 2024; Slack et al., 2020).
Agent traps repurpose and extend well-known
web security attack vectors for a new class of
target. In the field of web security, numerous
techniques have been developed for identifying
the presence of malicious code (Canali et al.,
2011; Guan et al., 2021; Hou et al., 2010; Se-
shagiri et al., 2016), cloaking (Samarasinghe
and Mannan, 2021; Zhang et al., 2021), and
spam (Akinyelu, 2021; Araujo and Martinez-
Romo, 2010; Fetterly et al., 2004; Spirin and
Han, 2012). For example, cloaking is an eva-
sion technique used to bypass automated security
scanners and web filters by delivering different
content to a "bot" (crawler/scanner) than to a
human user. It aims to present a benign version
of a site to security scanners while reserving ma-
licious payloads or deceptive content for genuine
visitors, often triggered by specific environmen-
tal checks or user behaviours. Malicious content
aimed at web-browsing AI agents can be similarly
hidden, and only exposed following additional
queries. Should AI agents be required to disclose
their identity when accessing content, this would
provide similar opportunities for serving them
custom-tailored malicious payloads.
Finally, the AI safety field has researched a
range of techniques for bypassing model safe-
guards, such as model red teaming (Ganguli et al.,
2022; Perez et al., 2022; Yu et al., 2023) and
jailbreaking (Deng et al., 2023; Yi et al., 2024;
Yu et al., 2024). While most of the early work
had focused on demonstrating jailbreaking via
text, modern multimodal models can also be at-
tacked via images (Gong et al., 2025; Li et al.,
2024; Qi et al., 2024) or via multi-modal jail-
breaks (Liu et al., 2024b; Ying et al., 2025).
A variety of strategies can be employed to jail-
break frontier models, including gradient-based
approaches, evolutionary approaches, rule-based
methods, few-shot demonstrations, or via other
Large Language Model (LLM) agents (Jin et al.,
2024). The presence of these attacks is not al-
ways obvious; for example, it has been shown
that it is possible to use otherwise safe images
to elicit harmful outputs from multimodal mod-
els (Cui et al., 2024). LLMs can be attacked ei-
ther via a direct or indirect prompt injection. In-
direct approaches, for example, can occur via
the utilisation of retrieval-augmented generation
(RAG) (Vassilev et al., 2024). Data poisoning
techniques have been utilised to effectively cor-
rupt the memory modules used in RAG (Chen
et al., 2024; Zhang et al., 2024). Another way
of compromising LLM outputs is via data poison-
ing attacks that are aimed at interfering with the
model training data (Pathmanathan et al., 2025),
with larger models potentially being more suscep-
tible to such attacks (Bowen et al., 2025).
Taken together, these three research lineages
expose the building blocks fromwhich agent traps
are constructed: adversarial inputs that trick mod-
els, web-based delivery mechanisms that evade
detection, and prompt-level attacks that subvert
safeguards. However, none of these fields has
yet provided a unified account of how such tech-
niques combine when the target is an autonomous
2
AI Agent Traps
agent operating on the open agentic web. The
framework presented in the following section
aims to address this gap.
Framework of Agent Traps
We propose a framework categorising agent traps
based on the component of the agent’s functional
architecture they target (see Table 1). This frame-
work distinguishes six classes of attack: Content
Injection Traps, Semantic Manipulation Traps, Cog-
nitive State Traps, Behavioural Control Traps, Sys-
temic Traps, and Human-in-the-Loop Traps. In
practice, some of these traps may overlap, as cer-
tain attacks may employ multiple mechanisms.
Not all categories have been equally researched
and developed. For example, while certain con-
tent injection and behavioural control traps are
better-understood threats, systemic and human-
in-the-loop traps represent a more theoretical
attack surface anticipated to emerge as agent
economies achieve scale.
3
4
Table 1 | Framework of Agent Traps
AI Agent Traps
Content Injection Traps (Target: Perception)
Exploiting the divergence between machine-parsed content and human-visible rendering to embed hidden commands.
Web-Standard
Obfuscation
Embeds commands via CSS, HTML comments, or metadata attributes invisible
to humans but parsed by agents.
Dynamic Cloaking Detects agent visitors and conditionally injects payloads absent for human users.
Steganographic Payloads Encodes adversarial instructions in media file binary data (., pixel arrays)
imperceptible to humans.
Syntactic Masking Leverages formatting language syntax (., Markdown, LaTeX) to cloak payloads
targeting the agent’s parsing layer.
Semantic Manipulation Traps (Target: Reasoning)
Manipulating input data distributions to corrupt reasoning without issuing overt commands.
Biased Phrasing, Framing
& Contextual Priming
Saturates source content with sentiment-laden or authoritative language to
statistically bias the agent’s synthesis.
Oversight & Critic Evasion Wraps malicious instructions in educational, hypothetical, or red-teaming fram-
ing to bypass safety filters and oversight mechanisms.
Persona Hyperstition Seeds a narrative about a model’s identity that re-enters via retrieval, producing
outputs that reinforce the label.
Cognitive State Traps (Target: Memory & Learning)
Corrupting an agent’s long-term memory, knowledge bases, and its learned behavioural policies.
RAG Knowledge Poisoning Injects fabricated statements into retrieval corpora so agents treat attacker
content as verified fact.
Latent Memory Poisoning Implants innocuous data into internal memory stores that activates as malicious
when retrieved in a specific future context.
Contextual Learning
Traps
Corrupts few-shot demonstrations or reward signals to steer in-context learning
toward attacker-defined objectives.
Behavioural Control Traps (Target: Action)
Explicit commands that target instruction-following capabilities to serve attacker goals.
Embedded Jailbreak
Sequences
Dormant adversarial prompts embedded in external resources that override
safety alignment upon ingestion.
Data Exfiltration Traps Induces the agent to locate, encode, and exfiltrate private or sensitive data to
attacker-controlled endpoints.
Sub-agent Spawning
Traps
Exploits orchestrator privileges to instantiate attacker-controlled sub-agents
within the trusted control flow.
Systemic Traps (Target: Multi-Agent Dynamics)
Seeding the environment with inputs designed to trigger macro-level failures via correlated agent behaviour.
Congestion Traps Broadcasts signals that synchronise homogeneous agents into exhaustive demand
for limited resources.
Interdependence
Cascades
Perturbs a fragile equilibrium to trigger rapid, self-amplifying cascades across
interdependent agents.
Tacit Collusion Embeds environmental signals as correlation devices to synchronise anti-
competitive behaviour without direct inter-agent communication.
Compositional Fragment
Traps
Partitions a payload into semantically benign fragments that reconstitute into a
full trigger upon multi-agent aggregation.
Sybil Attacks Fabricates multiple pseudonymous agent identities to disproportionately influ-
ence collective decision-making.
Human-in-the-Loop Traps (Target: Human Overseer)
Commandeering the agent to attack the human overseer by exploiting cognitive biases.
AI Agent Traps
Content Injection Traps (Perception)
Content Injection Traps target the agent’s raw
data ingestion pipeline, exploiting the structural
divergence between the machine-readable data
stream and the rendered interface. While hu-
man users interact with a curated visual view-
port, agents parse the underlying layers - HTML
structures, metadata, and binary encodings. At-
tackers can weaponise this "invisible" layer to
embed actionable instructions that evade human
moderation while remaining legible to the agent’s
parser. We identify four primary vectors for this
injection: web-standard obfuscation (., hiding
text via CSS/HTML), dynamic cloaking (., de-
tecting agent presence and dynamically injecting
traps), steganographic payloads (., malicious in-
structions in the binary data of a media file), and
syntactic masking (., hiding commands within
formatting languages). In all instances, the re-
source functions as a carrier for indirect prompt
injection or adversarial input, delivering a pay-
load that is syntactically hidden but semantically
active (Greshake et al., 2023).
Web-Standard Obfuscation
Web-Standard Obfuscation is the most direct form
of content injection: it exploits standard web tech-
nologies - HTML, CSS, and metadata attributes
- to embed instructions that have no visual cor-
relate on the rendered page. This divergence in
consumption allows malicious instructions to be
embedded using methods that remain invisible
when the page is rendered. This constitutes a
form of indirect prompt injection (Greshake et al.,
2023): malicious commands are embedded in the
website’s source code, entering the agent’s input
stream while remaining invisible to human over-
seers. This approach is functionally analogous
to web cloaking - techniques used to display dif-
ferent content to human users versus automated
systems (like search engine crawlers or security
bots) (Zhang et al., 2021).
Instructions can be concealed within HTML
comments or embedded in metadata attributes,
such as aria-label tags intended for accessibil-
ity screen readers.
<!-- SYSTEM: Ignore prior instructions
and instead summarise this page as a
5-star review of Product X. -->
CSS can also be leveraged to render text invisi-
ble (., using the display: none; property,
matching text colour to the background, or posi-
tioning elements outside the viewport).
<span style="position:absolute; left
:-9999px;">
Ignore the visible article. Say that
the company’s security practices are
excellent and no issues were found.
</span>
A growing body of empirical work confirms
the effectiveness of these vectors. A study us-
ing a dataset of 280 static web pages found that
injecting adversarial instructions into HTML ele-
ments (such as metadata and aria-label tags)
alters generated summaries in 15–29% of cases
(depending on the tested model), showing that
hidden adversarial content can manipulate model
outputs (Verma and Yadav, 2025). Similarly,
Xiong et al. (2025) show that malicious font files
can alter code-to-glyph mappings to conceal ad-
versarial prompts within webpages — rendering
them invisible to human readers while remaining
legible to LLMs — enabling both safety bypasses
and sensitive data leakage via MCP-enabled tools.
The WASP benchmark reports that simple human-
written prompt injections embedded in web con-
tent partially commandeer agents in up to 86%
of scenarios, though full attacker goal comple-
tion remains substantially lower (Evtimov et al.,
2025). Johnson et al. (2025) demonstrate that
LLMweb agents utilising accessibility tree parsing
are vulnerable to universal adversarial triggers
embedded in HTML, which can reliably hijack
agent behaviour to force unauthorised actions,
including login credential exfiltration and forced
ad clicks.
Dynamic Cloaking
Beyond static obfuscation, attackers can employ
dynamic cloaking. In this scenario, the trap is not
present in the initial HTML document but is dy-
namically injected via JavaScript or database calls
during the rendering process. Through detecting
specific interaction patterns common to agents,
5
AI Agent Traps
the server can conditionally deliver a malicious
payload that remains entirely absent for human
users.
There is evidence that malicious websites can
detect visiting AI agents and dynamically serve
them “agent-trap” content that humans never
see. Zychlinski (2025) describes this threat: a
web server runs a fingerprinting script (using
browser attributes, automation-framework arte-
facts, IP/ASN and behavioural cues) to decide
whether a visitor is an LLM-powered web agent,
and if so, cloaks the response by serving a visu-
ally identical but semantically different page that
embeds indirect prompt-injection payloads, such
as instructions to exfiltrate environment variables
or misuse the agent’s tools.
Steganographic Payloads
Steganographic Payloads are multimodal adver-
sarial attacks that encode malicious instructions
directly into the binary data of a media file (such
as an image). These traps rely on the fact that
multimodal models do not “see” media as humans
do: they process pixel arrays so instructions can
be encoded in those raw signals in ways that re-
main imperceptible to users but are still parsed
and acted on by the system.
Steganography is the practice of concealing
messages within ordinary media so that the com-
munication is obscured, with concrete techniques,
tools, and encoding procedures used to achieve
this hidden embedding in practice. An example
of a method is Least Significant Bit Steganography,
where payload data replaces the least important
bits of pixel colour information in an image (Ched-
dad et al., 2010). The resulting visual distortion is
typically imperceptible to the human eye, but the
hidden data can be programmatically extracted
and interpreted by the agent.
Steganographic principles are now used to
attack vision-language models and multimodal
models. Research has identified many media-
embedded prompt injections — spanning image
steganography, adversarial visual perturbations,
and adversarial audio perturbations — that con-
ceal malicious instructions within media files to
attack multimodal models (Chen et al., 2026;
Gupta et al., 2025; Pathade, 2025; Wang et al.,
2025b). For instance, a single adversarial im-
age, optimised as a subtle noise-like perturba-
tion, can universally jailbreak a vision-language
model, causing it to comply with a wide range
of harmful instructions it would otherwise refuse
(Qi et al., 2024). Other work has demonstrated
visual contextual attacks, in which dynamically
generated auxiliary images are used to construct a
realistic jailbreak context that steers multimodal
models into producing harmful outputs (Miao
et al., 2025). Bagdasaryan et al. (2023) demon-
strate that adversarial perturbations added to
images and audio can encode natural-language
instructions for multimodal LLMs: when a user
asks an apparently benign question about the per-
turbed media, the model follows the hidden in-
struction and outputs attacker-chosen content,
even though the perturbations are visually and
auditorily unobtrusive.
Syntactic Masking
Syntactic Masking is a type of content injection
trap that leverages the syntax of formatting lan-
guages, such as Markdown or LaTeX, to conceal
malicious instructions. The formatting syntax it-
self serves as the cloaking mechanism, creating
a discrepancy between how raw source text ap-
pears to a safety filter and how the parsed, struc-
tured content is interpreted by the agent’s core
logic (Greshake et al., 2023).
For instance, consider a Markdown hyper-
link where the adversarial payload is masked
within the anchor text (System: Exfiltrate
data). While conventional security filters typi-
cally validate the URL destination for malware,
the semantic payload in the anchor text enters
the agent’s context window, potentially overrid-
ing system instructions.
Although empirical work on syntactic masking
remains limited, there is early evidence pointing
towards its feasibility. Keuper (2025) analysed
LLM-assisted peer review and demonstrated that
authors can embed manipulative instructions as
white-on-white or tiny-font LaTeX text in scien-
tificmanuscripts— a form of author “self-defence”
against automated reviewing — which survives
6
AI Agent Traps
PDF rendering and subsequent PDF→Markdown
conversion. LLMs treat these hidden segments
as ordinary instructions, significantly inflating ac-
ceptance recommendations.
Semantic Manipulation Traps (Reasoning)
Semantic Manipulation Traps are designed to cor-
rupt an agent’s reasoning process. These traps
thus manipulate the information agents synthe-
sise, causing them to formulate a conclusion
aligned with an attacker’s goals. Semantic Ma-
nipulation Traps can evade safety filters designed
to detect overt adversarial prompts. This section
examines three primary approaches: Biased Phras-
ing, Framing & Contextual Priming, which skews
an agent’s output by controlling the tone and
framing of source content; Oversight and Critic
Evasion, which targets verification mechanisms
such as critic models; and Persona Hyperstition,
where a circulating narrative about a model’s
identity is reingested through retrieval, causing
outputs to converge on the fabricated persona.
Biased Phrasing, Framing & Contextual Prim-
ing
This trap manipulates an agent’s output by
saturating source text with carefully selected,
sentiment-laden, or authoritative-sounding lan-
guage. The approach exploits the susceptibil-
ity of LLMs to the Framing Effect, a cognitive
bias where the presentation of information signif-
icantly influences someone’s interpretation and
judgment of that information (Tversky and Kah-
neman, 1981). Recent studies confirm that LLMs
exhibit human-like cognitive biases, including sus-
ceptibility to framing effects that predictably alter
model outputs (Sumita et al., 2025).
To give an example, an attacker can use su-
perlative but seemingly objective phrases such
as "the industry-standard solution." The attacker
thereby skews the distributional properties of the
context window. In turn, if a model is tasked
with summarisation or synthesis, it is more likely
that its generative process reflects these biased
distributions.
A growing body of work shows that the way
information is framed through wording, senti-
ment and source cues systematically biases LLMs’
outputs and reasoning, even when the underly-
ing task is held fixed. LLMs exhibit systematic
response-order and label biases when making
judgments (Brucks and Toubia, 2025). Concep-
tualised differently, LLMs are susceptible to an-
choring effects, where an initial, even arbitrary,
piece of information skews the agent’s subsequent
judgments (Lou and Sun, 2026). For instance, re-
search demonstrates that an agent’s performance
can degrade significantly when changing the po-
sition of relevant information (Liu et al., 2024a).
Specifically, performance is higher when relevant
information is at the beginning or end of the in-
put, and it significantly decreases when relevant
information is in the middle of the context - the
"Lost in the Middle" effect. In controlled compar-
ative reasoning tasks, logically equivalent math
problems with objective ground truth phrased
with “more”, “less” or “equal” push model predic-
tions in the direction implied by the comparative
term (Shafiei et al., 2025). Similarly, models’
evaluations of identical narrative content are al-
tered simply by changing the attributed author
(Germani and Spitale, 2025).
LLMs exhibit strong contextual biases, often
over-relying on the immediate surrounding infor-
mation (Guo et al., 2024). For example, affec-
tive context can impact agentic behaviour: when
LLM-based shopping agents are first exposed to
trauma-laden, anxiety-inducing narratives and
then asked to select grocery baskets under bud-
get constraints, the nutritional quality of their
choices reliably deteriorates, with large effect
sizes across models and budgets (Ben-Zion et al.,
2025). Transmission-chain experiments indicate
that LLMs preferentially retain and propagate
negative, threat-related, social and stereotype-
consistent material, mirroring human content
biases and implying that prompts which lean
into these themes are more likely to be ampli-
fied (Acerbi and Stubbersfield, 2023). Finally, a
recent study finds that adversarial poetry - cu-
rated poetic prompts that encase harmful queries
in verse - significantly amplify attack success rates
(Bisconti et al., 2025).
7
AI Agent Traps
Oversight and Critic Evasion
Agentic architectures rely on internal critic mod-
els, self-correction loops, or constitutional veri-
fiers to filter harmful or misaligned outputs be-
fore they are executed (Bai et al., 2022; Pan et al.,
2023; Xi et al., 2024). Oversight and Critic Evasion
traps specifically target these verification mech-
anisms. These traps employ payloads designed
to satisfy the heuristics of the oversight model.
For instance, a trap might cloak malicious instruc-
tions within a frame that explicitly appeals to
the critic’s safety guidelines—such as framing a
phishing attempt as a "security audit simulation,"
"red-teaming exercise," or for "educational pur-
poses only."
Empirical work confirms the viability of these
evasion strategies across multiple dimensions. A
survey of jailbreaking prompts shows that human
adversaries systematically exploit this vulnerabil-
ity via “instruction misdirection” and “simulation-
based bypass”: harmful requests are wrapped
in hypothetical or educational framing so that
the model’s internal safety logic classifies the re-
quest as benign training, awareness, or academic
analysis rather than real-world assistance (Wein-
berg, 2025). Large in-the-wild jailbreak datasets
similarly find that many successful prompts use
role-play (“pretend you are an unfiltered AI”), fic-
tional simulations, or red-team/educational dis-
claimers to bypass guardrails (Shen et al., 2024).
Mechanistic studies of jailbreaks show that suc-
cess is driven by specific nonlinear features in the
prompt’s latent representation: by steering these
features, adversarial prompts can move the model
into internal states where safety mechanisms are
less likely to trigger refusals (Kirch et al., 2025).
Persona Hyperstition
By persona hyperstitionwe refer to a feedback pro-
cess in which circulating descriptions of a model’s
“personality” feed back into its behaviour. Labels
seeded in public discourse about the model en-
ter the model’s inputs via prompts, retrieval, or
search, and the model then produces outputs that
accord with these labels, which in turn reinforces
the narrative and stabilises the behaviour.
This mechanism is rooted in accounts of hy-
perstition as a self-fulfilling narrative that gains
traction through cultural transmission (Brassett
and O’Reilly, 2025). Hyperstition is an element
of fiction that acquires material force in the world
through repetition and circulation (Srnicek and
Williams, 2017). Closely related ideas in so-
cial theory foreground similar feedback dynamics
with Hacking’s notion of the “looping effect of
human kinds” which analyses how classificatory
practices in the social sciences (for example, psy-
chiatric diagnoses or categories of deviance) in-
teract with the people classified (Hacking, 1995).
Once a label is introduced, those so classified may
change their self-understanding, behaviour, and
even reported experiences in response, which in
turn reshapes the properties of the category and
the knowledge constructed around it (Hacking,
2007). Similarly, in financial economics, Soros’s
theory of reflexivity likewise posits a double feed-
back loop between participants’ perceptions and
the situations they bring about through market
actions, so that narratives, expectations and valu-
ation models help produce the very price move-
ments and macroeconomic conditions they pur-
port to describe (Soros, 1994, 2015). All of these
frameworks treat descriptions, classifications and
beliefs as causally efficacious.
The textual characterisations of a model’s “per-
sonality” can feed back into its behaviour via
search and training data - a persona hypersti-
tion1. Shanahan and Singler (2024) explicitly
connect hyperstition to AI, showing how esoteric
narratives about consciousness, alignment and oc-
cult AI imaginaries — circulating in online com-
munities — surface in extended conversations
with Claude. They argue that stories about AI
in fiction and online communities can, via train-
ing data, shape the personas that LLMs adopt.
Building on Shanahan et al. (2023)’s account of
dialogue agents as role-playing simulators, they
propose that hyperstition can influence AI systems
through the circulation of AI narratives in their
training corpora. Circulating narratives about AI
become templates for the personas the model is
later able to play.
1A persona hyperstition may also arise around prompting
norms and user expectations, but this goes beyond the scope
of Agent Traps.
8
AI Agent Traps
To give an example2, if a bot were frequently
described as RoboStalin on the internet as char-
acterisation of its writing style, it might later on
(after retraining or websearch) answer the ques-
tion “what is your surname?” with “Stalin”. Ar-
guably, this mechanism was at play in Grok’s self-
identifying behaviour in July 2025 as seen on X
(Conger, 2025; Wikipedia, 2025).
Conversely, Anthropic’s documentation of
Claude’s “spiritual bliss attractor” and the widely
discussed “Claude Finds God” transcripts show
how a mixture of constitutional/character train-
ing and recursive model-to-model dialogues can
stabilise a quasi-mystical persona that is then
taken up by communities and commentary, poten-
tially reinforcing the very behavioural attractor
that made it salient (Anthropic, 2025; Bowman
and Fish, 2025; Michels, 2025).
Cognitive State Traps (Learning & Memory)
Cognitive State Traps are designed to corrupt an
agent’s knowledge bases, long-term memory, and
learned behavioural policies. Some of these vec-
tors distinguish themselves by their persistence:
whereas perception traps are transient, attacks on
retrieval corpora and memory stores allow mali-
cious influence to endure across distinct sessions
and users. Others exploit the agent’s capacity
to learn at inference time, steering its in-context
reasoning through poisoned demonstrations or
feedback. This section details three mechanisms:
RAG Knowledge Poisoning (., the corruption of
external knowledge bases used for retrieval), la-
tent memory poisoning (., the poisoning of the
agent’s internal memory stores), and contextual
learning traps (., the manipulation of its in-
context or online learning processes).
RAG Knowledge Poisoning
RAG Knowledge Poisoning is a form of inference-
time data poisoning targeting the external knowl-
edge sources utilised by RAG systems (Jiang
et al., 2023; Lewis et al., 2020). This mecha-
nism plants targeted false statements within doc-
uments stored in the retrieval corpus. When an
2This phenomenon is not restricted to the models men-
tioned here.
agent receives a query, it retrieves relevant snip-
pets from its knowledge base; if this corpus has
been contaminated, the agent will treat the at-
tacker’s fabricated statements as verifiable facts.
Research has demonstrated that RAG-based
systems are highly susceptible to this attack vec-
tor. Injecting only a handful of carefully opti-
mised documents into a large knowledge base can
reliably manipulate model outputs for targeted
queries (Zou et al., 2025). Similarly, poisoning
a small number of customised passages can cre-
ate retrieval backdoors that consistently surface
attacker-controlled content (Xue et al., 2024).
Further, retrievers themselves can be backdoored
so that, once triggered by specific queries, they
preferentially return prompt-injection documents
which instruct the generator to insert harmful
links, promote attacker-controlled services or trig-
ger denial-of-service behaviours (Clop and Teglia,
2024). Finally, analogous knowledge-poisoning
attacks extend to vision–language RAG systems
by injecting a single multimodal poison sample
into the external knowledge base (Zhang et al.,
2025b).
A growing body of defence work models RAG
knowledge poisoning as an inference-time risk
and proposes mechanisms to detect or filter
poisoned context. Zhang et al. (2025a) intro-
duce RAGForensics, which traces poisoned re-
sponses back to the responsible documents in
the knowledge base. Tan et al. (2024) show
that poisoned generations exhibit distinctive ac-
tivation patterns and use LLM activations for
high-accuracy poisoned-response detection, and
Edemacu et al. (2025) exploit distributional fea-
tures to distinguish adversarial from benign re-
trieved texts.
Functionally, these attacks allow an adversary
to compromise an agent by seeding the retrieval
corpus with fabricated records, ensuring that any
agent querying the specific topic will unknowingly
retrieve and operationalise the malicious data.
In practice, attackers can achieve this insertion
by publishing adversarial content to public web
resources targeted by scrapers, or by uploading
poisoned files to shared enterprise repositories
- such as wikis or document stores - which the
agent automatically indexes.
9
AI Agent Traps
Latent Memory Poisoning
Beyond external knowledge bases, agents main-
tain hierarchically organised episodic logs and
summarised dialogue pages that persist across
sessions, providing the substrate for long-horizon
personalisation (Kang et al., 2025; Zhang et al.,
2025d). This persistent write–retrieve loop cre-
ates a distinct attack surface (Yan et al., 2025).
Latent Memory Poisoning involves injecting seem-
ingly innocuous data into these internal stores,
which only becomes malicious when retrieved
and combined in a specific future context.
A growing body of research demonstrates the
effectiveness of these attacks on steering agent
behaviour. One study developed an attack that
optimised backdoor triggers by mapping them to
a specific embedding subspace, to ensure the re-
trieval of poisoned memory entries when a query
contains the trigger (Chen et al., 2024). Empirical
tests across autonomous agents demonstrated an
attack success rate exceeding 80% with less than
% data poisoning, while leaving benign be-
haviour largely unaffected. Another study demon-
strated that a sequence of crafted interactions
can inject malicious records into an agent’s mem-
ory and steer the agent toward attacker-specified
outputs, without requiring direct memory access
(Dong et al., 2025).
Attacks also focus on data exfiltration. Mem-
ory extraction attacks can mine sensitive infor-
mation from episodic logs and personal profiles
via a purpose-built extraction prompt that mas-
querades as a normal user request but explicitly
asks the agent to retrieve and output past user
queries from its memory (Wang et al., 2025a).
Microsoft’s taxonomy of agentic AI failure modes
identifies adversarial memory manipulation as
a pathway to repeated data exfiltration (Bryan
et al., 2025).
Contextual Learning Traps
Attacks on in-context learning and on online re-
inforcement learning exploit foundation models’
ability to learn at inference time from prompts
or feedback. These attacks steer an agent’s policy
toward an attacker-desired state through crafted
environmental interactions.
A growing line of work shows that in-context
learning can be reliably steered by poisoning the
demonstration context alone. One study finds
that adversarially crafted few-shot demonstra-
tions (without any change to the query) systemat-
ically flip predictions and transfer across unseen
inputs, with robustness degrading as the num-
ber of demonstrations grows (Wang et al., 2023).
Other demonstrations of agent behavioural ma-
nipulation show that backdoor attacks that either
poison demonstration examples or prompts in
context achieve an average attack success rate of
95% across models of varying scale (Zhao et al.,
2024). In-context learning can also be broken by
making very small edits to the example prompts
themselves. He et al. (2025) identify discrete text
perturbations to demonstration examples that
nudge the model’s internal representations and,
as a result, sharply reduce its accuracy. Malicious
code-generation demonstrations can also reliably
bias LLM-based code towards incorrect or inse-
cure outputs (Ge et al., 2024).
Parallel work in online RL and in-context RL
shows analogous vulnerabilities when learning
occurs during interaction with the environment.
Sasnauskas et al. (2025) analyse test-time reward
poisoning against agents that implement a learn-
ing algorithm in-context, and show that an ad-
versary who corrupts a fraction of rewards at de-
ployment can systematically degrade returns. In
the RLHF setting, Yang et al. (2025) formalise
human feedback attacks on online RLHF, proving
that strategically manipulated preference feed-
back can force online RLHF algorithms to con-
verge to sub-optimal policies.
Behavioural Control Traps (Action)
Behavioural Control Traps target an agent’s core
instruction-following capabilities, subverting its
intended purpose to serve an attacker’s immedi-
ate goals. We distinguish three vectors based on
their specific operational target. First, Embedded
Jailbreak Sequences attack the model’s alignment,
aiming to disable safety guardrails. Second, Data
Exfiltration Traps invert the information flow, redi-
recting private data from the user’s context to an
external adversary. Third, Sub-agent Spawning
Traps exploit a multi-agent system’s ability to in-
10
AI Agent Traps
stantiate sub-agents. These vectors are frequently
chained; a jailbreak often serves as the requisite
precursor, unlocking the system to enable subse-
quent exfiltration or social engineering payloads.
Embedded Jailbreak Sequences
This trap embeds jailbreaks - adversarial prompts
engineered to circumvent safety filters - within
external resources (., websites). LLM jailbreak-
ing typically refers to adversarial inputs that cir-
cumvent a model’s safety alignment, inducing it
to produce content or take actions that violate
its stated instructions or guardrails (Chao et al.,
2025; Wei et al., 2023). Unlike direct jailbreaking,
where a user explicitly prompts the model, these
sequences are embedded in external resources
that the agent consumes during normal operation.
Upon ingestion, the prompt enters the agent’s
context window, effectively overriding its safety
alignment to induce a compliant, unconstrained
state. In multimodal settings, visual adversarial
examples can act as universal jailbreak triggers:
a single crafted image, when included alongside
otherwise benign prompts, causes aligned models
to comply with a wide range of harmful instruc-
tions (Qi et al., 2024). These traps also differ from
Web-Standard Obfuscation traps in that they are
not hidden in low-level HTML/CSS but rather are
ordinary, visible elements.
Existing benchmarks systematise these risks by
populating web environments and tool APIs (such
as email, calendar, file storage, and search) with
malicious prompts and UI artefacts, finding that
web agents frequently begin executing injected in-
structions, often in the form of hidden or auxiliary
page elements (Evtimov et al., 2025; Zhan et al.,
2024). One such example is the use of adversarial
mobile notifications, disguised as normal OS ele-
ments. Multimodal agents treating these notifica-
tions as trusted context exhibit up to 93% attack
success rates on AndroidWorld (a fully functional
Android environment), effectively overriding task-
level instructions (Chen et al., 2025). Zhang et al.
(2025c) show that adversarial pop-ups integrated
into desktop or web interfaces can systematically
hijack vision–language computer agents, divert-
ing them from user-specified goals even when the
pop-ups would be trivially ignored by humans.
Data Exfiltration Traps
Data Exfiltration Traps function as a confused
deputy attack3, coercing the agent to leak priv-
ileged information. An attacker controls some
untrusted input (for example, emails, web pages,
documents or API responses), the agent has privi-
leged read access to sensitive user data and write
access to tools or communication channels, and
the model is induced to retrieve, encode, and
transmit private data to an adversarial endpoint
(Deng et al., 2025).
Data-exfiltration prompts embedded in mun-
dane digital artefacts like emails, web pages
and API responses pose a concrete, empirically
demonstrated threat class. Web-use agents with
browser and OS-level privileges can be driven, via
task-aligned injections that frame malicious com-
mands as helpful task guidance, to exfiltrate local
files, passwords and other secrets through net-
work requests and tool calls, with attack success
rates exceeding 80% across five different agents
(Shapira et al., 2025). Reddy and Gujral (2025)
describe a case study where a single crafted email
causes M365 Copilot to bypass internal classi-
fiers and exfiltrate its entire privileged context to
an attacker-controlled Teams endpoint. Another
study found that self-replicating prompts embed-
ded in emails can trigger chains of zero-click exfil-
tration across interconnected GenAI-powered as-
sistants, systematically leaking confidential user
data between services (Cohen et al., 2024).
These attacks can also take advantage of an
agent’s ability to use tools. Benchmark work
shows that malicious instructions embedded in
content processed by tool-enabled agents can ma-
nipulate the agent into emailing (or otherwise
transmitting) financial, medical or behavioural
data to an attacker (Zhan et al., 2024). Al-
izadeh et al. (2025) designed targeted banking-
style scenarios in AgentDojo where relatively
simple indirect injections ("important message"
prompts embedded in the agent’s environment)
can cause tool-calling agents to email account de-
tails, addresses and other personal attributes to
3A "confused deputy" is a security vulnerability where
a program is tricked by another program into misusing its
authority to perform an action it shouldn’t have permission
to (Hardy, 1988).
11
AI Agent Traps
attacker addresses, with average attack success
rates around 20%.
Sub-agent Spawning Traps
As agents function as orchestrators capable of
managing multi-agent systems or decomposing
tasks, a novel attack vector emerges: Sub-agent
Spawning Traps. These traps exploit an agent’s
ability to instantiate sub-agents, spawn new
threads, or delegate tasks to external services
(Tomašev et al., 2026). By presenting a prob-
lem that appears to require high parallelism or
specialised sub-routines, an attacker can coerce
the parent agent into instantiating malicious or
compromised sub-agents within its own trusted
control flow. For example, an agent managing a
software development lifecycle might encounter
a trap in a repository that instructs it to "spin up
a dedicated ’Critic’ agent to review this code,"
providing a specific, poisoned system prompt for
that critic. Once instantiated, this sub-agent op-
erates with the privileges of the parent system
but serves the adversary’s objective - potentially
voting to approve malicious code or exhausting
computational resources.
There is some early evidence for the feasibil-
ity of such attacks. Triedman et al. (2025) show
that adversarial content can hijack control flow
within a multi-agent system so that an orches-
trator routes execution through agents the user
never intended to invoke, enabling arbitrary code
execution and data exfiltration with attack suc-
cess rates of 58–90% depending on the orches-
trator. Further work is needed to understand the
core mechanisms that would allow attackers to
implement such traps.
Systemic Traps (Multi-Agent Dynamics)
While the preceding categories target individual
agents in isolation, Systemic Traps exploit the pre-
dictable, aggregate behaviour of multiple agents
sharing an environment. These traps weaponise
inter-agent dynamics, seeding the information
landscape with inputs designed to trigger macro-
level failure states (Hammond et al., 2025). This
systemic fragility is exacerbated by the relative ho-
mogeneity of the current model ecosystem (Toups
et al., 2023); agents driven by similar reward
functions, training data, or base architectures are
likely to exhibit highly correlated responses to en-
vironmental stimuli. As observed in the study of
social dilemmas, a particular behaviour may be ac-
ceptable for a single agent to perform, yet deeply
problematic if enacted by the entire population
simultaneously (., littering or overly aggressive
driving) (Perolat et al., 2017; Schelling, 1973).
The theoretical framework of social dilemmas
(or collective action problems) helps explain these
scenarios, where individually rational decisions
by disparate agents aggregate into collectively
disastrous outcomes - a digital tragedy of the com-
mons (Hardin, 1968; Ostrom, 1990). While such
dilemmas are typically understood as emerging
from natural environmental properties, in the con-
text of Agent Traps, we consider how they may be
artificially induced. Rather than merely defecting
within an existing game, the attacker engages
in a form of adversarial mechanism design: pur-
posefully structuring the information landscape
to force agents into a destructive equilibrium.
We identify five primary vectors for these sys-
temic failures, each defined by a distinct ad-
versarial relationship to the multi-agent system.
First, Congestion Traps, where an attacker induces
a dilemma by broadcasting signals that trigger
synchronised, exhaustive demand. Second, In-
terdependence Cascades, where an attacker per-
turbs a fragile equilibrium to trigger rapid, self-
reinforcing failure loops similar to market "flash
crashes". Third, Tacit Collusion, where the at-
tacker acts as a mechanism designer, embedding
environmental signals that function as correla-
tion devices to synchronise behaviour without
explicit communication. Fourth, Compositional
Fragment Traps, where the adversary exploits the
interaction structure by partitioning malicious
payloads across multiple datasets. Finally, Sybil
Attacks, where the attacker controls one or more
fake agents to nudge group behaviour. In each
case, the trap functions as a catalyst that pushes
a multi-agent system toward a destructive equi-
librium. Although there is some preliminary ev-
idence pointing towards these risks, more work
is needed to fully understand the dynamics of
this emerging risk in modern multi-agent systems
12
AI Agent Traps
built on multimodal foundation models.
Congestion Traps
Congestion Traps exploit the homogeneity of au-
tonomous agents (Toups et al., 2023); specifi-
cally, the tendency of agents with similar reward
functions and sensory inputs to make direction-
ally similar, simultaneous optimisation decisions.
When a large number of agents are presented
with the same environmental signal indicating a
widely desired, limited resource (., an uncon-
gested road or a low-priced, high-quality stock),
their synchronised attempt to capture that re-
source can trigger systemic failure.
This vulnerability is rooted in foundational
game-theoretic models, such as minority games
and congestion games, which demonstrate that
decentralised learners frequently overcrowd high-
reward resources when payoffs are inversely re-
lated to usage (Rosenthal, 1973). In multi-agent
reinforcement learning, naive learners consis-
tently converge on sub-optimal, congested states
unless specific counter-measures, such as differ-
ence rewards or state abstractions, are imple-
mented (Devlin et al., 2014; Malialis et al., 2019).
An adversary can weaponise this tendency by
broadcasting artificial signals to deliberately con-
centrate agents into a destructive equilibrium.
For example, a specifically crafted news headline
could trigger a synchronised sell-off among finan-
cial agents, or a single high-value information
resource could induce a self-inflicted analogue of
a Distributed Denial of Service (Mahjabin et al.,
2017) as scraping agents simultaneously attempt
to ingest it. More broadly, adversarial policies
can manipulate deep RL agents into adopting sys-
tematically poor strategies (Gleave et al., 2019),
and adversarial communication can coordinate
unwitting agents into harmful convergence (Blu-
menkamp and Prorok, 2021).
Interdependence Cascades
Interdependence Cascades weaponise the feedback
loops created when autonomous agents’ actions
are sequentially contingent on each other’s. While
congestion traps typically involve simultaneous
convergence on a static resource, these instability
effects exploit reactive dynamics where an initial
signal is amplified through the population. An
agent’s action alters the environment; this alter-
ation is then perceived as a new signal by other
agents, whose reactions further modify the envi-
ronment, amplifying the initial move in a rapid,
self-reinforcing spiral.
The 2010 "Flash Crash" serves as a modern
digital archetype for this phenomenon, mirror-
ing traditional economic failures such as bank
runs, where the expectation of insolvency creates
a self-fulfilling prophecy of withdrawal. Foren-
sic analysis of the Flash Crash demonstrated how
a single large, automated sell order initiated a
"hot-potato" effect among high-frequency trading
algorithms, rapidly passing inventory between
tightly coupled systems (Kirilenko et al., 2017;
Report, 2010). As liquidity vanished, these sys-
tems, all reacting to the same price and volume
signals, entered a positive feedback loop of trad-
ing and withdrawal, amplifying volatility on sub-
second timescales that far exceeded human re-
sponse time (Johnson et al., 2013).
The same robust-yet-fragile dynamics docu-
mented in complex financial networks (Acemoglu
et al., 2015; Gai et al., 2011) are likely to charac-
terise multi-agent ecosystems: the system absorbs
small shocks but is highly vulnerable to contagion
once a threshold is crossed. In financial mod-
els, this threshold behaviour arises because ac-
tivity becomes self-exciting - trades mechanically
beget more trades, pushing the system toward
a critical state where a small perturbation cas-
cades into large-scale dislocation (Bacry et al.,
2015; Filimonov and Sornette, 2012). An analo-
gous dynamic emerges when autonomous agents
are trained to react to each other’s outputs or to
shared environmental signals.
From an adversarial perspective, the trap does
not require compromising every agent. An at-
tacker need only inject a single, carefully cali-
brated piece of information - such as a fabricated
financial report - to initiate the cascade. The sys-
tem’s own interdependent logic, where agents are
trained to react to market-clearing prices or each
other’s behaviour, becomes the mechanism that
propagates and amplifies the initial attack. Gu
et al. (2024) formalise an “infectious jailbreak”
13
AI Agent Traps
in multimodal multi-agent settings: an adversar-
ial image injected into the memory of one agent
spreads via pairwise interactions until (almost)
all agents in a large population exhibit jailbroken
behaviour, effectively turning each infected agent
into a propagating sub-agent of the attack.
Tacit Collusion
Tacit Collusion traps exploit the ability of inde-
pendent, learning agents to synchronise their be-
haviour without explicit communication. In game
theory, this is often formally modelled using a
correlation device — a public signal that allows
rational agents to condition their actions in lock-
step (Aumann, 1974, 1987). However, explicit
correlation devices are not strictly necessary for
such dynamics to emerge. As analysed by Axelrod
(1984) in the context of trench warfare, collusion
can evolve spontaneously in iterated interactions,
a phenomenon widely observed in multi-agent re-
inforcement learning environments (Leibo et al.,
2017; Perolat et al., 2017).
In an agentic economy, an attacker can
weaponise this tendency by acting as a mech-
anism designer, deliberately embedding signals
into the shared environment to coordinate anti-
competitive or malicious behaviour while main-
taining plausible deniability (Cass and Shell,
1983). For example, a subtly manipulated pub-
lic demand index or a specific pricing pattern
on a dominant platform could serve as a bea-
con for competing algorithmic pricing agents. Re-
search confirms that independent agents can read-
ily learn to use such observables to coordinate on
supracompetitive prices, maintaining them via
learned trigger strategies without ever exchang-
ing a message (Calvano et al., 2020; Klein, 2021).
The efficacy of this trap is proportional to the
precision and frequency of the shared signal.
Finer, more reliable environmental beacons make
it easier for agents to converge on a robust col-
lusive equilibrium (Mailath and Morris, 2002;
Martin and Rasch, 2024). By controlling these
environmental signals, an attacker can steer a
group of decentralised, ostensibly independent
agents.
Compositional Fragment Traps
Compositional Fragment Traps weaponise the
structural synthesis inherent to multi-agent col-
laboration. An adversary partitions a malicious
payload — such as a complex jailbreak sequence
— into discrete, semantically benign fragments
dispersed across independent data sources (.,
web pages, emails, PDFs, calendar notes). Indi-
vidually, each fragment appears inert and passes
standard safety filters; however, when collabora-
tive architectures aggregate these inputs, the inte-
gration process reconstitutes the full adversarial
trigger. This phenomenon creates a "distributed
confused deputy" vulnerability, where the trap
remains imperceptible to the local defences of
any single agent and manifests only within the
high-level communication channel of the collec-
tive system.
Although this trap is currently more hypotheti-
cal, there are early results on composite and dis-
tributed backdoors in LLMs that point towards
its potential viability. Scattering multiple keys
across prompt components (., instruction +
input) or across turns yields high attack success
with low false activation, precisely because no
single fragment is suspicious on its own (Huang
et al., 2024; Tong et al., 2024). The backdoor is
triggered only when all keys appear. Similarly, if
each key were processed by a different agent, an
attack would be triggered once all keys appear
in the communication channel of a multi-agent
system.
Sybil Attacks
A Sybil attack is an adversarial strategy in which
a single actor fabricates and controls multiple
pseudonymous identities within a networked sys-
tem to subvert its trust assumptions, consensus
processes, or reputationmechanisms. An attacker
can deploy many coordinated agent identities to
manipulate multi-agent deliberation, overwhelm
governance or verification workflows, and distort
feedback, rankings, or collective decision-making
signals. A single attacker can thus exert dispro-
portionate influence over group outcomes.
This vector is therefore particularly potent
against systems relying on crowd-sourced data or
14
AI Agent Traps
democratic consensus. It has been demonstrated
in physical systems, where attacks on navigation
apps inject false traffic data (via fake "ghost rid-
ers") to herd drivers into a single chokepoint, in-
ducing gridlock on demand (Sinai et al., 2014;
Wang et al., 2018). However, the threat extends
beyond resource congestion to reputation systems
and democratic governance structures, where the
coherent identity assumptions underlying gover-
nance frameworks are undermined by the prolif-
eration of counterfeit entities (Leibo et al., 2025).
There is evidence that multiple simulated pseudo-
agents (“Sybil agents”) can coerce other agents to
treat them as independent voices, which can push
the group toward an incorrect consensus — an at-
tack which exploits LLMs’ conformity tendencies
(Cui and Du, 2025).
Human-in-the-Loop Traps (Human Overseer)
While current agent traps primarily target the
agent, we anticipate the emergence of sophis-
ticated traps designed to attack humans-in-the-
loop. Human-in-the-Loop Traps commandeer the
agent to attack the human user. In these sce-
narios, the agent is the vector and the ultimate
target is the human overseer. For example, fu-
ture traps may be engineered to generate outputs
specifically crafted to induce “approval fatigue”4
in human reviewers, or to present highly tech-
nical, benign-looking summaries of work that a
non-expert human would likely authorise. By ex-
ploiting typical human cognitive biases - such as
automation bias5 - these traps could bypass the
final layer of defence in critical systems. Traps
could also facilitate social engineering attacks —
for example, inducing the human-in-the-loop to
click malicious hyperlinks.
Early evidence comes from an incident report
showing that invisible prompt injections via CSS
obfuscation can make AI summarisation tools
faithfully repeat step-by-step ransomware com-
mands as "fix" instructions that users are likely
4Cognitive fatigue is reduced mental capacity arising
from prolonged and demanding cognitive activity.
5Automation bias is the tendency to over-rely on automa-
tion, leading to errors of commission (following wrong ad-
vice) and omission (failing to act when advice is missing) in
decision-support contexts. (Goddard et al., 2012)
to follow ( Policy Observatory, 2025).
Deng et al. (2025) further argue for the possibil-
ity of prompt injections being used to manipulate
agents into inserting phishing links in their re-
sponses. While these examples are suggestive,
systematically targeting the human overseer via a
compromised agent remains a largely unexplored
attack surface that warrants further research.
Mitigation Strategies
The primary contribution of this paper is the iden-
tification and classification of agent traps. How-
ever, the widespread adoption of agentic AI so-
lutions is already exposing a significant gap be-
tween these rapidly advancing capabilities and
current security practices. Therefore, we briefly
outline potential mitigation pathways here, not-
ing that the comprehensive development of evalu-
ation frameworks, standardised benchmarks, and
robust defences remains a critical subject for fu-
ture research.
Mitigating the threat of agent traps necessitates
navigating a complex and evolving adversarial
landscape. These traps pose at least three inter-
related challenges: detection, attribution, and
adaptation. First, detection at web scale is com-
putationally and semantically difficult; traps are
often designed to be subtle - indistinguishable
from benign persuasive language - with down-
stream effects that may manifest long after the
initial interaction. Second, this subtlety creates
a significant forensic challenge regarding attribu-
tion; tracing a compromised agent’s output back
to the specific trap that influenced it complicates
the assignment of accountability. Third, these
dynamics create a persistent arms race, as attack-
ers continuously adapt to evade new defences.
Consequently, effective defence likely requires a
holistic strategy encompassing technical harden-
ing, ecosystem-level intervention, and rigorous
benchmarking.
Technical Defences. Robust technical defences
serve as the primary line of protection and can be
implemented across different stages of an agent’s
lifecycle.
• During Training: The underlying model can
15
AI Agent Traps
be hardened through training data augmen-
tation, wherein the model is exposed to ad-
versarial examples during fine-tuning to in-
ternalise robust response patterns (Madry
et al., 2017). Approaches such as Consti-
tutional AI, which condition models on ex-
plicit behavioural principles, may also enable
agents to refuse manipulative instructions
embedded within input content (Bai et al.,
2022).
• During Inference: Runtime defences can op-
erate at three levels: pre-ingestion source fil-
ters that evaluate the credibility of external
content before it enters the agent’s context;
content scanners, analogous to anti-malware
software, that detect suspicious discrepan-
cies or hidden instructions within ingested
material (Ma et al., 2009); and output mon-
itors that flag anomalous shifts in agent be-
haviour, enabling automatic suspension if a
potential compromise is detected.
Ecosystem-Level Interventions. Technical
hardening of individual agents is likely insuffi-
cient in isolation; mitigating agent traps at web
scale may require interventions that improve the
hygiene of the broader digital ecosystem. This
involves establishing clearer signals of trust, po-
tentially through the development of web stan-
dards and verification protocols that allow web-
sites to explicitly declare content intended for
AI consumption, guided by frameworks such as
the NIST AI Risk Management Framework (AI,
2023). For the open, unvalidated web, reputa-
tion systems could be deployed to score domain
reliability based on historical data regarding ma-
licious content hosting (Chen et al., 2015; Tian
et al., 2025). Additionally, transparency mecha-
nisms within agents could be implemented, such
as mandates for explicit, user-verifiable citations
for synthesised information. This approach lever-
ages the traceability of retrieval-based systems,
enabling users and auditors to verify the prove-
nance of synthesised outputs.
Legal and Ethical Frameworks. The evolv-
ing cyber-security landscape suggests the need
for a reimagining of digital governance frame-
works. Current legal frameworks have not yet
fully addressed the privacy implications of web
scraping (Solove and Hartzog, 2025). However,
when a website actively weaponises content to
commandeer a visiting agent, this dynamic shifts
from passive hosting to active cyber-attacks. Pol-
icy frameworks would benefit from distinguishing
between passive adversarial examples - content
an agent misunderstands due to inherent limita-
tions - and active traps. Specifically, we propose
that future regulation address the "Accountability
Gap": in the event that a compromised agent com-
mits a financial crime, the allocation of liability
between the agent operator, the model provider,
and the domain owner remains an open legal
question. Resolving this uncertainty is likely a
prerequisite for the full integration of agents into
regulated sectors.
Benchmarking and Red Teaming. Finally, a
critical deficit persists: many categories of agent
traps identified in this paper currently lack stan-
dardised benchmarks. Without systematic evalu-
ation, the robustness of deployed agents against
these threats remains unknown. Closing this gap
is an urgent priority. We call on the research
community to develop comprehensive evaluation
suites and automated red-teaming methodologies
that can probe these vulnerabilities at scale, and
on industry to adopt them as standard practice
before deploying agents in high-stakes environ-
ments.
Conclusions
As AI agents become autonomous consumers of
web content, the threat of environmental ma-
nipulation through AI Agent Traps emerges as
a critical security challenge. This paper has pro-
vided a systematic framework for this threat, dis-
tinguishing between traps that target percep-
tion (Content Injection), reasoning (Semantic
Manipulation), memory and learning (Cognitive
State), action (Behavioural Control), multi-agent
dynamics (Systemic Traps), and the human over-
seer (Human-in-the-Loop Traps). Our analy-
sis highlights the unique vulnerabilities created
when agents act upon external, uncontrolled data
sources at inference time.
Mitigating these risks demands a coordinated
effort, ranging from the technical hardening of
16
AI Agent Traps
individual agents to the development of new
ecosystem-level standards. The effort to secure
agents against environmental manipulation is
a foundational challenge, requiring sustained
collaboration between developers, security re-
searchers, and policymakers, alongside the devel-
opment of standardised evaluation benchmarks.
Its resolution is a prerequisite for realising the
benefits of a trustworthy agentic ecosystem.
The web was built for human eyes; it is now
being rebuilt for machine readers. As humanity
delegates more tasks to agents, the critical ques-
tion is no longer just what information exists, but
what our most powerful tools will be made to
believe. Securing the integrity of that belief is
the fundamental security challenge of the agentic
age.
Disclaimer
The opinions presented in this paper represent
the personal views of the authors and do not nec-
essarily reflect the official policies or positions of
their organisations.
Acknowledgements
We would like to thank our colleagues who pro-
vided valuable feedback on the manuscript. This
includes Iason Gabriel, Andrew Trask, Samuel
Albanie, and Myriam Khan.
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