Detecting Unwanted Location Trackers M. Delano
Internet-Draft Swarthmore College
Intended status: Informational J. Lowell
Expires: 2 September 2025 National Network to End Domestic Violence
S. Prabhu
Nokia
1 March 2025
DULT Threat Model
draft-ietf-dult-threat-model-01
Abstract
Lightweight location tracking tags are in wide use to allow users to
locate items. These tags function as a component of a crowdsourced
tracking network in which devices belonging to other network users
(e.g., phones) report which tags they see and their location, thus
allowing the owner of the tag to determine where their tag was most
recently seen. While there are many legitimate uses of these tags,
they are also susceptible to misuse for the purpose of stalking and
abuse. A protocol that allows others to detect unwanted location
trackers must incorporate an understanding of the unwanted tracking
landscape today. This document provides a threat analysis for this
purpose, will define what is in and out of scope for the unwanted
location tracking protocols, and will provide some design
considerations for implementation of protocols to detect unwanted
location tracking.
About This Document
This note is to be removed before publishing as an RFC.
The latest revision of this draft can be found at https://ietf-wg-
dult.github.io/threat-model/draft-ietf-dult-threat-model.html.
Status information for this document may be found at
https://datatracker.ietf.org/doc/draft-ietf-dult-threat-model/.
Discussion of this document takes place on the Detecting Unwanted
Location Trackers Working Group mailing list (mailto:unwanted-
trackers@ietf.org), which is archived at
https://mailarchive.ietf.org/arch/browse/unwanted-trackers/.
Subscribe at https://www.ietf.org/mailman/listinfo/unwanted-
trackers/.
Source for this draft and an issue tracker can be found at
https://github.com/ietf-wg-dult/draft-ietf-dult-threat-model.
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Status of This Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Conventions and Definitions . . . . . . . . . . . . . . . . . 4
2.1. Conventions . . . . . . . . . . . . . . . . . . . . . . . 4
2.2. Definitions . . . . . . . . . . . . . . . . . . . . . . . 4
3. Security Considerations . . . . . . . . . . . . . . . . . . . 5
3.1. Taxonomy of unwanted tracking . . . . . . . . . . . . . . 5
3.1.1. Example scenarios with analyses TODO: expand scenarios
to incorporate expanded taxonomy . . . . . . . . . . 9
3.1.2. Bluetooth vs. other technologies . . . . . . . . . . 13
3.2. Possible Methods to Circumvent DULT Protocol . . . . . . 14
3.2.1. Threat Prioritization Framework for DULT Threat
Model . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2.2. Threat Matrix . . . . . . . . . . . . . . . . . . . . 14
3.2.3. Deploying Multiple Tags . . . . . . . . . . . . . . . 17
3.2.4. Remote Advertisement Monitoring . . . . . . . . . . . 17
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3.2.5. Non-compliant tags . . . . . . . . . . . . . . . . . 18
3.2.6. Misuse of Remote Disablement . . . . . . . . . . . . 18
3.2.7. Rotating Tracker IDs . . . . . . . . . . . . . . . . 18
3.2.8. Delayed Activation of Trackers . . . . . . . . . . . 19
3.2.9. Multi-Tag Correlation Attack . . . . . . . . . . . . 19
3.2.10. Exploiting Gaps in OS-Based Detection . . . . . . . . 19
3.2.11. Spoofing Legitimate Devices . . . . . . . . . . . . . 20
3.2.12. Heterogeneous Tracker Networks . . . . . . . . . . . 20
3.3. What is in scope . . . . . . . . . . . . . . . . . . . . 20
3.3.1. Technologies . . . . . . . . . . . . . . . . . . . . 20
3.3.2. Attacker Profiles . . . . . . . . . . . . . . . . . . 20
3.3.3. Victim Profiles . . . . . . . . . . . . . . . . . . . 21
3.4. What is out of scope . . . . . . . . . . . . . . . . . . 21
3.4.1. Technologies . . . . . . . . . . . . . . . . . . . . 21
3.4.2. Attack Profiles . . . . . . . . . . . . . . . . . . . 21
3.4.3. Victim Profiles . . . . . . . . . . . . . . . . . . . 21
4. Design Considerations . . . . . . . . . . . . . . . . . . . . 21
4.1. Design Requirements . . . . . . . . . . . . . . . . . . . 22
4.1.1. Detecting Unwanted Location Tracking . . . . . . . . 22
4.1.2. Finding Tracking Tags . . . . . . . . . . . . . . . . 24
4.1.3. Disabling Tracking Tags . . . . . . . . . . . . . . . 25
4.1.4. Privacy and Security Requirements (TODO) . . . . . . 25
4.2. Design Constraints . . . . . . . . . . . . . . . . . . . 25
4.2.1. Bluetooth constraints . . . . . . . . . . . . . . . . 25
4.2.2. Power constraints . . . . . . . . . . . . . . . . . . 26
4.2.3. Device constraints . . . . . . . . . . . . . . . . . 27
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 28
6. Normative References . . . . . . . . . . . . . . . . . . . . 28
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 28
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 28
1. Introduction
Location tracking tags are widely-used devices that allow users to
locate items. These tags function as a component of a crowdsourced
tracking network in which devices belonging to other network users
(e.g., phones) report on the location of tags they have seen. At a
high level, this works as follows:
* Tags ("accessories") transmit an advertisement payload containing
accessory-specific information. The payload also indicates
whether the accessory is separated from its owner and thus
potentially lost.
* Devices belonging to other users ("non-owner devices") observe
those payloads and if the payload is in a separated mode, reports
its location to some central service.
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* The owner queries the central service for the location of their
accessory.
A naive implementation of this design exposes both a tag’s user and
anyone who might be targeted for location tracking by a tag’s user,
to considerable privacy risk. In particular:
* If accessories simply have a fixed identifier that is reported
back to the tracking network, then the central server is able to
track any accessory without the user's assistance, which is
clearly undesirable.
* Any attacker who can guess a tag ID can query the central server
for its location.
* An attacker can surreptitiously plant an accessory on a target and
thus track them by tracking their "own" accessory.
In order to minimize these privacy risks, it is necessary to analyze
and be able to model different privacy threats. This document uses a
flexible framework to provide analysis and modeling of different
threat actors, as well as models of potential victims based on their
threat context. It defines how these attacker and victim persona
models can be combined into threat models. It is intended to work in
concert with the requirements defined in
[I-D.detecting-unwanted-location-trackers], which facilitate
detection of unwanted tracking tags.
2. Conventions and Definitions
2.1. Conventions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
2.2. Definitions
* *active scanning*: a search for location trackers manually
initiated by a user
* *passive scanning*: a search for location trackers running in the
background, often accompanied by notifications for the user
* *tracking tag*: a small device that is not easily discoverable and
transmits location data to other devices.
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* *easily discoverable*: device is larger than 30 cm in at least one
dimension, device is larger than 18 cm x 13 xm in two of its
dimensions, device is larger than 250 cm^3 in three-dimensional
space
3. Security Considerations
Incorporation of this threat analysis into the DULT protocol does not
introduce any security risks not already inherent in the underlying
Bluetooth tracking tag protocols. Existing attempts to prevent
unwanted tracking by the owner of a tag have been criticized as
potentially making it easier to engage in unwanted tracking of the
owner of a tag. However, Beck et al. have demonstrated
(https://eprint.iacr.org/2023/1332.pdf) a technological solution that
employs secret sharing and error correction coding.
3.1. Taxonomy of unwanted tracking
To create a taxonomy of threat actors, we can borrow from Dev et
al.’s Models of Applied Privacy (MAP) framework
(https://dl.acm.org/doi/fullHtml/10.1145/3544548.3581484). This
framework is intended for organizations and includes organizational
threats and taxonomies of potential privacy harms. Therefore, it
cannot be applied wholesale. However, its flexibility, general
approach to personas, and other elements, are applicable or can be
modified to fit the DULT context.
The characteristics of threat actors may be described as follows.
This is not intended to be a full and definitive taxonomy, but an
example of how existing persona modeling concepts can be applied and
modified.
* Expertise level
- Expert: The attacker works in or is actively studying computer
science, networking, computer applications, IT, or another
technical field.
- Non-expert: The attacker does not work or study in, or is a
novice in, a technical field.
* Proximity to victim
- High: Lives with victim or has easy physical access to victim
and/or victim’s possessions.
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- Medium: Has some physical access to the person and possessions
of someone who lives with victim, such as when the attacker and
victim are co-parenting a child.
- Low: Does not live with or have physical access to victim and/
or victim’s possessions.
* Access to resources
- High: The attacker has access to resources that may amplify the
impact of other characteristics. These could include, but are
not limited to, funds (or control over “shared†funds), persons
assisting them in stalking behavior, or employment that
provides privileged access to technology or individuals’
personal information.
- Low: The attacker has access to few or no such resources.
In addition, the victim also has characteristics which influence the
threat analysis. As with attacker characteristics, these are not
intended as a definitive taxonomy.
* Expertise level
- Expert: The victim works in or is actively studying computer
science, networking, computer applications, IT, or another
technical field.
- Non-expert: The victim does not work or study in, or is a
novice in, a technical field.
* Expectation of unwanted tracking
- Suspecting: The victim has reason to believe that unwanted
tracking is a likely risk.
- Unsuspecting: The victim has no particular reason to be
concerned about unwanted tracking.
* Access to resources
- High: The victim is generally able to safely access practical
and relevant resources. These might include funds to pay a car
mechanic or private investigator, law enforcement or legal
assistance, or other resources.
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- Low: The victim is generally unable to safely access practical
and relevant resources. These might include money to pay a car
mechanic or private investigator, law enforcement or legal
assistance, or other resources.
* Access to technological safeguards
- High: The victim is able to safely use, and has access to,
technological safeguards such as active scanning apps.
- Limited: The victim is able to safely use, and has access to,
technological safeguards such as active scanning apps, but is
unable to use their full capacity.
- Low: The victim is not able to use technological safeguards
such as active scanning apps, due to reasons of safety or
access.
It is also appropriate to define who is using the tracking tags and
incorporate this into a model. This is because if protocols overly
deprioritize the privacy of tracking tags’ users, an attacker could
use a victim’s own tag to track them. Beck et al. describe a
possible technological solution
(https://eprint.iacr.org/2023/1332.pdf) to the problem of user
privacy vs privacy of other potential victims. In designing the
protocol, these concerns should be weighed equally. TODO: Is this
actually how we want to weigh them? This warrants further
discussion.
* Tracking tag usage
- Attacker only: The attacker controls one or more tracking tags,
but the victim does not.
- Victim only: The victim controls one or more tracking tags, but
the attacker does not.
- Attacker and victim: Both the attacker and victim control one
or more tracking tags.
Any of the threat analyses above could be affected by placement of
the tag(s). For instance, a tag could be placed on a victim's
person, or in proximity to a victim but not on their person (e.g. a
child's backpack). Examples include:
* Tag placement
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- Tag on victim's person or immediate belongings. This attack
vector allows an attacker to track a victim in a fine-grained
way. It is also more likely that this attack would trigger an
alert from the tag.
- Tag(s) in proximity to victim but not on their person (e.g.
child's backpack, car). While this is a less fine-grained
attack, it may also be less likely to be discovered by the
victim. A child may not realize the significance of an alert
or know how to check for a tag. A parent may not think to scan
for such a tag, or may have more difficulty finding a tag in a
complex location such as a car.
- Tags nearby but not used for unwanted location tracking (e.g.
false positives by companions or on transit). While this is
not an attack vector in its own right, repeated false positives
may discourage a victim from treating alerts seriously.
- Multiple tags using multiple types of placement. This attack
vector may trick a victim into believing that they have fully
addressed the attack when they have not. It also allows for a
diversity of monitoring types (e.g. monitoring the victim's
precise location, monitoring a child's routine, monitoring car
usage).
Anticipation, or lack thereof, of unwanted tracking, may affect the
threat and the attack. If the victim does not realize that they are
at risk (as is common early in an abusive relationship, or if a
victim is being stalked by a stranger), they may be less likely to
use active scanning tools or understand the implications of detected
tags.
* Anticipation of unwanted tracking
- High anticipation: The victim believes that an attacker is
tracking or may be tracking their location.
- Low anticipation: The victim is unaware or does not believe
that an attacker is tracking or may be tracking their location.
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3.1.1. Example scenarios with analyses TODO: expand scenarios to
incorporate expanded taxonomy
The following scenarios are composite cases based upon reports from
the field. They are intended to illustrate different angles of the
problem. They are not only technological, but meant to provide
realistic insights into the constraints of people being targeted
through these tags. There is no identifying information for any real
person contained within them. In accordance with research on how
designers understand personas (https://dl.acm.org/
doi/10.1145/2207676.2208573), the characters are given non-human
names without attributes such as gender or race. The analysis of
each scenario provides an example usage of the modeling framework
described above. It includes a tracking tag usage element for
illustrative purposes. However, as discussed previously, this
element becomes more or less relevant depending on protocol
evolution. Note that once a given attacker persona has been modeled,
it could be recombined with a different victim persona, or vice
versa, to model a different scenario. For example, a non-expert
victim persona could be combined with both non-expert and expert
attacker personas.
3.1.1.1. Scenario 1
3.1.1.1.1. Narrative
Mango and Avocado have two young children. Mango, Avocado, and the
children all use smartphones, but have no specialized technical
knowledge. Mango left because Avocado was abusive. They were
homeless for a month, and the children have been living with Avocado.
They now have an apartment two towns away. They do not want Avocado
to know where it is, but they do want to see the children. They and
Avocado meet at a public playground. They get there early so that
Avocado will not see which bus route they arrived on and keep playing
with the children on the playground until after Avocado leaves, so
that Avocado will not see which bus route they get on. Two days
later, Avocado shows up at Mango’s door, pounding on the door and
shouting.
3.1.1.1.2. Analysis
In this case, the attacker has planted a tag on a child. Co-
parenting after separation is common in cases of intimate partner
violence where the former partners have a child together. Child
visits can be an opportunity to introduce technology for purposes of
stalking the victim.
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+=====================+============================================+
| Attacker Profile | Avocado |
+=====================+============================================+
| Expertise Level | Non-Expert |
+---------------------+--------------------------------------------+
| Proximity to Victim | Medium |
+---------------------+--------------------------------------------+
| Access to Resources | Unknown, but can be presumed higher than |
| | Mango’s due to Mango’s recent homelessness |
+---------------------+--------------------------------------------+
Table 1
+====================================+============+
| Victim Profile | Mango |
+====================================+============+
| Expertise Level | Non-Expert |
+------------------------------------+------------+
| Access to Resources | Low |
+------------------------------------+------------+
| Access to Technological Safeguards | Normal |
+------------------------------------+------------+
Table 2
+=======================+===================+
| Other Characteristics | Avocado and Mango |
+=======================+===================+
| Accessory Usage | Attacker Only |
+-----------------------+-------------------+
Table 3
3.1.1.2. Scenario 2
3.1.1.2.1. Narrative
Strawberry and Elderberry live together. Neither has any specialized
technological knowledge. Strawberry has noticed that Elderberry has
become excessively jealous – every time they go to visit a friend by
themselves, Elderberry accuses them of infidelity. To their alarm,
over the last week, on multiple occasions, Elderberry has somehow
known which friend they visited at any given time and has started to
harass the friends. Strawberry eventually gets a notification that a
tracker is traveling with them, and thinks it may be in their car,
but they cannot find it. They live in a car-dependent area and
cannot visit friends without the car, and Elderberry controls all of
the “family†money, so their cannot take the car to the mechanic
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without Elderberry knowing.
3.1.1.2.2. Analysis
Here, the attacker and the victim are still cohabiting, and the
attacker is monitoring the victim’s independent activities. This
would allow the attacker to know if, for instance, the victim went to
a police station or a domestic violence agency. The victim has
reason to think that they are being tracked, but they cannot find the
device. This can happen if the sound emitted by the device is
insufficiently loud, and is particularly a risk in a car, where seat
cushions or other typical features of a car may provide sound
insulation for a hidden tag. The victim could benefit from having a
mechanism to increase the volume of the sound emitted by the tag.
Another notable feature of this scenario is that because of the
cohabitation, the tag will spend most of the time in “near-owner
state†as defined by the proposed industry consortium specification
[I-D.detecting-unwanted-location-trackers]. In near-owner state it
would not provide alerts under that specification.
+=====================+============+
| Attacker Profile | Elderberry |
+=====================+============+
| Expertise Level | Non-Expert |
+---------------------+------------+
| Proximity to Victim | High |
+---------------------+------------+
| Access to Resources | High |
+---------------------+------------+
Table 4
+====================================+===================+
| Victim Profile | Strawberry |
+====================================+===================+
| Expertise Level | Non-Expert |
+------------------------------------+-------------------+
| Access to Resources | Low |
+------------------------------------+-------------------+
| Access to Technological Safeguards | Impaired (cannot |
| | hear alert sound) |
+------------------------------------+-------------------+
Table 5
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+=======================+===========================+
| Other Characteristics | Elderberry and Strawberry |
+=======================+===========================+
| Accessory Usage | Attacker Only |
+-----------------------+---------------------------+
Table 6
3.1.1.3. Scenario 3
3.1.1.3.1. Narrative
Lime and Lemon have been dating for two years. Lemon works for a
tech company and often emphasizes how much more they know about
technology than Lime, who works at a restaurant. Lemon insists on
having access to Lime’s computer and Android phone so that they can
“make sure they are working well and that there are no dangerous
apps.†Lemon hits Lime when angry and has threatened to out Lime as
gay to their conservative parents and report them to Immigration &
Customs Enforcement if Lime “talks back.†Lime met with an advocate
at a local domestic violence program to talk about going to their
shelter once a bed was available. The advocate did some safety
planning with Lime, and mentioned that there is an app for Android
that can scan for location trackers, but Lime did not feel safe
installing this app because Lemon would see it. The next time Lime
went to see the advocate, they chose a time when they knew Lemon had
to be at work until late to make sure that Lemon did not follow them,
but when Lemon got home from work they knew where Lime had been.
3.1.1.3.2. Analysis
This is a case involving a high-skill attacker, with a large skill
difference between attacker and victim. This situation often arises
in regions with a high concentration of technology industry workers.
It also may be more common in ethnic-cultural communities with high
representation in the technology industry. In this case the victim
is also subject to a very high level of control from the attacker due
to their imbalances in technological skills and societal status, and
is heavily constrained in their options as a result. It is unsafe
for the victim to engage in active scanning, or to receive alerts on
their phone. The victim might benefit from being able to log into an
account on another phone or a computer and view logs of any recent
alerts collected through passive scanning.
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+=====================+========+
| Attacker Profile | Lemon |
+=====================+========+
| Expertise Level | Expert |
+---------------------+--------+
| Proximity to Victim | High |
+---------------------+--------+
| Access to Resources | High |
+---------------------+--------+
Table 7
+====================================+============+
| Victim Profile | Lime |
+====================================+============+
| Expertise Level | Non-Expert |
+------------------------------------+------------+
| Access to Resources | Low |
+------------------------------------+------------+
| Access to Technological Safeguards | Low |
+------------------------------------+------------+
Table 8
+=======================+================+
| Other Characteristics | Lemon and Lime |
+=======================+================+
| Accessory Usage | Attacker Only |
+-----------------------+----------------+
Table 9
3.1.2. Bluetooth vs. other technologies
The above taxonomy and threat analysis focus on location tracking
tags. They are protocol-independent; if a tag were designed using a
technology other than Bluetooth, they would still apply. The key
attributes are the functionalities and physical properties of the
accessory from the user’s perspective. The accessory must be small
and not easily discoverable and able to transmit its location to
other consumer devices.
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3.2. Possible Methods to Circumvent DULT Protocol
There are several different ways an attacker could attempt to
circumvent the DULT protocol in order to track a victim without their
consent. These include deploying multiple tags to follow a single
victim and using a non-compliant tag (e.g. speaker disabled, altered
firmware, spoofed tag). There are also other potential concerns of
abuse of the DULT Protocol, such as remotely disabling a victim's
tracking tag.
3.2.1. Threat Prioritization Framework for DULT Threat Model
Threats in the DULT ecosystem vary in severity, feasibility, and
likelihood, affecting users in different ways. Some threats enable
long-term tracking, while others exploit gaps in detection mechanisms
or leverage non-compliant devices. To assess and prioritize these
risks, the following framework classifies threats based on their
impact, likelihood, feasibility, affected users, and the availability
of mitigations. A Threat Matrix is included that provides a
structured assessment of known threats and their associated risks.
This categorization helps in understanding the challenges posed by
different tracking techniques and their potential mitigations.
3.2.2. Threat Matrix
To systematically assess the risks associated with different threats,
we introduce the following threat matrix. This categorization
considers key risk factors:
* Impact: The potential consequences of the threat if successfully
exploited.
- Low: Minimal effect on privacy and security.
- Medium: Moderate effect on user privacy or tracking protection.
- High: Severe privacy violations or safety risks.
* Likelihood: The probability of encountering this threat in real-
world scenarios.
- Low: Rare or requires specific conditions.
- Medium: Possible under common scenarios.
- High: Frequently occurring or easily executed.
* Feasibility: The difficulty for an attacker to execute the attack.
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- Easy: Requires minimal effort or common tools.
- Moderate: Needs some technical expertise or resources.
- Hard: Requires significant effort, expertise, or access.
* Risk Level: A qualitative assessment based on impact, likelihood,
and feasibility.
- Low: Limited risk requiring minimal mitigation.
- Medium: Requires mitigation to prevent common attacks.
- High: Critical threat needing immediate mitigation.
* Affected Users: These are categorized as either:
- Victims: Individuals specifically targeted or affected by the
attack.
- All users: Anyone using the system, even if they are not
directly targeted.
* Mitigation Available?: Whether a known mitigation strategy exists.
- Yes: A viable mitigation exists.
- Partial: Some mitigations exist but are not fully effective.
- No: No effective mitigation currently available.
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+=============+======+==========+===========+======+========+==========+
|Threat |Impact|Likelihood|Feasibility|Risk |Affected|Mitigation|
| | | | |Level |Users |Available?|
+=============+======+==========+===========+======+========+==========+
|Deploying |High |High |Easy |High |Victims |Partial |
|Multiple Tags| | | | | | |
+-------------+------+----------+-----------+------+--------+----------+
|Remote |High |High |Easy |High |All |No |
|Advertisement| | | | |users | |
|Monitoring | | | | | | |
+-------------+------+----------+-----------+------+--------+----------+
|Non-Compliant|High |Medium |Moderate |Medium|Victims |No |
|Tags | | | | | | |
+-------------+------+----------+-----------+------+--------+----------+
|Misuse of |Medium|Medium |Moderate |Medium|Victims |Partial |
|Remote | | | | | | |
|Disablement | | | | | | |
+-------------+------+----------+-----------+------+--------+----------+
|Rotating |High |High |Easy |High |Victims |Partial |
|Tracker IDs | | | | | | |
+-------------+------+----------+-----------+------+--------+----------+
|Delayed |Medium|High |Easy |High |Victims |No |
|Activation of| | | | | | |
|Trackers | | | | | | |
+-------------+------+----------+-----------+------+--------+----------+
|Multi-Tag |High |Medium |Moderate |Medium|Victims |No |
|Correlation | | | | | | |
|Attack | | | | | | |
+-------------+------+----------+-----------+------+--------+----------+
|Exploiting |High |High |Moderate |High |All |Partial |
|Gaps in OS- | | | | |users | |
|based | | | | | | |
|Detection | | | | | | |
+-------------+------+----------+-----------+------+--------+----------+
|Spoofing |Medium|Medium |Moderate |Medium|Victims |No |
|Legitimate | | | | | | |
|Devices | | | | | | |
+-------------+------+----------+-----------+------+--------+----------+
|Heterogeneous|High |Medium |Hard |Medium|Victims |No |
|Tracker | | | | | | |
|Networks | | | | | | |
+-------------+------+----------+-----------+------+--------+----------+
Table 10
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3.2.3. Deploying Multiple Tags
When an attacker deploys tracking tags to follow a victim, they may
deploy more than one tag. For example, if planting a tracking tag in
a car, the attacker might place one tag inside the car, and another
affixed on the outside of the car. The DULT protocol must be robust
to this scenario. This means that scans, whether passive or active,
need to be able to return more than one result if a device is
suspected of being used for unwanted tracking, and the time to do so
must not be significantly impeded by the presence of multiple
trackers. This also applies to situations where many tags are
present, even if they are not being used for unwanted location
tracking, such as a busy train station or airport where tag owners
may or may not be in proximity to their tracking tags. This method
prolongs unwanted tracking, making it a high-impact threat, as it
enables continuous monitoring even if some tags are discovered.
Since multiple low-cost tags can be easily deployed, the likelihood
is high, and the feasibility is easy, given the availability of
commercially accessible trackers. As a result, the overall risk is
high, requiring robust countermeasures. While scanning for multiple
tags offers partial mitigation, sophisticated attackers may still
evade detection by distributing tags strategically.
3.2.4. Remote Advertisement Monitoring
Bluetooth advertisement packets are not encrypted, so any device with
Bluetooh scanning capabilities in proximity to a location tracking
tag can receive Bluetooth advertisement packets. If an attacker is
able to link an identifier in an advertisement packet to a particular
tag, they may be able to use this information to track the tag over
time, and potentially by proxy the victim or other individual,
without their consent. Tracking tags typically rotate any
identifiers associated with the tag, but the duration with which they
rotate could be up to 24 hours (see e.g.
[I-D.detecting-unwanted-location-trackers]). Beck et al. have
demonstrated (https://eprint.iacr.org/2023/1332.pdf) a technological
solution that employs secret sharing and error correction coding that
would reduce this to 60 seconds. However, work must investigate how
robust this scheme is to the presence of multiple tags (see
Section 3.2.3). This attack has a high impact, as it allows
persistent surveillance while circumventing built-in protections.
Given that capturing Bluetooth signals is trivial using common
scanning tools, the likelihood is high, and the feasibility is easy,
making it a high-risk attack. While rotating identifiers provides
partial mitigation, attackers can still use advanced correlation
techniques to bypass this defense.
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3.2.5. Non-compliant tags
An attacker might physically modify a tag in a way that makes it non-
compliant with the standard (e.g. disabling a speaker or vibration).
An attacker might make alterations to a tag's firmware that make it
non-compliant with the standard. This bypasses key protections
entirely, making the impact high by preventing victims from
discovering hidden trackers. Although technical modifications
require some expertise, they are achievable with moderate effort,
making the likelihood medium and feasibility moderate. As a result,
the overall risk remains medium. Current mitigation methods are
incomplete, though research continues on detecting such rogue
devices.
3.2.6. Misuse of Remote Disablement
An attacker might misuse remote disablement features to prevent a
victim detecting or locating a tag. This could be used to prevent a
victim locating an attacker's tag, or could be used by an attacker
against a victim's tag as a form of harassment. The ability to
disable a victim’s protections introduces a medium impact, while the
likelihood is medium, as it requires specific technical knowledge.
Since execution is moderately complex, feasibility is moderate,
leading to a medium-risk attack. While authentication measures can
partially mitigate this risk, these protections are not foolproof.
3.2.7. Rotating Tracker IDs
Attackers may use dynamic identifier changes, such as rotating
Bluetooth MAC addresses, to evade detection. This makes it difficult
for detection systems relying on persistent unknown device
identification. Pattern recognition techniques capable of detecting
clusters of devices exhibiting ID rotation behaviors can help
mitigate this. The impact is high, as it directly undermines
detection mechanisms, while the likelihood is high, given that this
is a widely used evasion technique. Since implementing ID rotation
is straightforward, feasibility is easy, making this a high-risk
attack. While time-based correlation methods offer partial
mitigation, an ongoing arms race exists between detection and
circumvention.
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3.2.8. Delayed Activation of Trackers
Some tracking devices remain inactive for extended periods before
starting to broadcast, making them harder to detect during initial
scans. This allows attackers to delay detection until the victim has
traveled a significant distance. Historical tracking behavior
analysis, rather than solely real-time scanning, is necessary to
mitigate this threat. The impact is medium, as it temporarily
bypasses detection systems, while the likelihood is high, given how
easy it is to implement such a delay in firmware. Since execution
requires minimal effort, feasibility is easy, making this a high-risk
attack. No effective mitigation currently exists, though long-term
behavioral analysis could help detect such trackers.
3.2.9. Multi-Tag Correlation Attack
By distributing multiple tracking tags across locations frequently
visited by a target (home, workplace, etc.), attackers can
reconstruct movement patterns over time. Traditional tracking
prevention measures focus on individual devices, making this method
difficult to counter. Cross-tag correlation analysis could improve
detection of recurring unknown trackers near a user. The impact is
high, as it enables persistent monitoring, while the likelihood is
medium, since multiple devices are required. Given that execution is
moderately complex, feasibility is moderate, leading to a medium-risk
attack. While no effective mitigation exists, coordinated scanning
across devices could help detect recurring unknown trackers.
3.2.10. Exploiting Gaps in OS-Based Detection
Some detection systems trigger alerts only under specific conditions,
such as when motion is detected. Attackers can adjust device
behavior to avoid detection during these periods. A more consistent,
vendor-independent approach to unwanted tracking alerts would help
reduce blind spots. The impact is high, as it results in gaps in
protection across different platforms, while the likelihood is high,
given OS fragmentation and differences in security policies. With
feasibility at a moderate level, this results in a high-risk attack.
While cross-platform threat modeling provides partial mitigation,
detection gaps remain.
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3.2.11. Spoofing Legitimate Devices
Attackers can modify tracker broadcasts to mimic common Bluetooth
devices, making them blend into their surroundings and evade
detection. Using machine-learning-based anomaly detection techniques
can help distinguish genuine devices from potential tracking
attempts. The impact is medium, as users may mistakenly assume the
tracker is a harmless device. However, the likelihood is medium,
since executing a convincing spoof is difficult, and the feasibility
is moderate, due to the technical requirements. This results in a
medium-risk attack. Currently, no effective mitigation exists.
3.2.12. Heterogeneous Tracker Networks
Attackers may use a mix of tracking devices from different
manufacturers (e.g., Apple AirTags, Tile, Samsung SmartTags) to
exploit gaps in vendor-specific tracking protections. Many detection
systems are brand-dependent, making them ineffective against mixed
tracker deployments. Establishing a cross-vendor framework for
detection and alerts would enhance protection. The impact is high,
as it circumvents traditional defenses, while the likelihood is
medium, since deploying multiple brands requires effort. With
feasibility at a hard level, this remains a medium-risk attack. No
effective mitigation currently exists, though research into cross-
technology threat detection is ongoing.
3.3. What is in scope
3.3.1. Technologies
The scope of this threat analysis includes any accessory that is
small and not easily discoverable and able to transmit its location
to other consumer devices.
3.3.2. Attacker Profiles
An attacker who deploys any of the attacks described in Section 3.2.1
is considered in scope. This includes attempts to track a victim
using a tracking tag and applications readily available for end-users
(e.g. native tracking application) is in scope. Additonally, an
attacker who physically modifies a tracking tag (e.g. to disable a
speaker) is in scope. An attacker who makes non-nation-state level
alterations to the firmware of an existing tracking tag or creates a
custom device that leverages the crowdsourced tracking network is in
scope.
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3.3.3. Victim Profiles
All victims profiles are in scope regardless of their expertise,
access to resources, or access to technological safeguards. For
example, protocols should account for a victim's lack of access to a
smartphone, and scenarios in which victims cannot install separate
software.
3.4. What is out of scope
3.4.1. Technologies
There are many types of technology that can be used for location
tracking. In many cases, the threat analysis would be similar, as
the contexts in which potential attackers and victims exist and use
the technology are similar. However, it would be infeasible to
attempt to describe a threat analysis for each possible technology in
this document. We have therefore limited its scope to location-
tracking accessories that are small and not easily discoverable and
able to transmit their locations to other devices. The following are
out of scope for this document:
* App-based technologies such as parental monitoring apps.
* Other Internet of Things (IoT) devices.
* Connected cars.
* User accounts for cloud services or social media.
3.4.2. Attack Profiles
Attackers with nation-state level expertise and resources who deploy
custom or altered tracking tags to bypass protocol safeguards or
jailbreak a victim end-device (e.g. smartphone) are considered out of
scope.
3.4.3. Victim Profiles
N/A
4. Design Considerations
As discussed in Section 3, unwanted location tracking can involve a
variety of attacker, victim, and tracking tag profiles. A successful
implementation to preventing unwanted location tracking should:
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* Include a variety of approaches to address different scenarios,
including active and passive scanning and notifications or sounds
* Account for scenarios in which the attacker has high expertise,
proximity, and/or access to resources within the scope defined in
Section 3.3 and Section 3.4
* Account for scenarios in which the victim has low expertise,
access to resources, and/or access to technological safeguards
within the scope defined in Section 3.3 and Section 3.4
* Avoid privacy compromises for the tag owner when protecting
against unwanted location tracking using tracking tags
4.1. Design Requirements
The DULT protocol should 1) allow victims to detect unwanted location
tracking, 2) help victims find tags that are tracking them, and 3)
provide instructions for victims to disable those trackers if they
choose. These affordances should be implemented while considering
the appropriate privacy and security requirements.
4.1.1. Detecting Unwanted Location Tracking
There are three main ways that the DULT protocol should assist
victims in detecting potentially unwanted location tracking: 1)
active scanning, 2) passive scanning, and 3) tracking tag alerts.
4.1.1.1. Active Scanning
There may be scenarios where a victim suspects that they are being
tracked without their consent. Active scanning should allow a user
to use a native application on their device to search for tracking
tags that are separated from their owners. Additional information
about when that tag has been previously encountered within a
designated time window (e.g. the last 12 hours) should also be
included if available (see Section 4.1.4). Allowing users to
"snooze" or ignore tags known to be safe (e.g. tags from a family
member) could also be implemented. Tracking tags that are near their
owners should not be shared to avoid abuse of the active scanning
feature.
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4.1.1.2. Passive Scanning
The platform should passively scan for devices suspected of unwanted
location tracking and notify the user. This will involve
implementing one or more algorithms to use to flag trackers and
determine when to notify the user. (A dedicated DULT WG document
will address tracking algorithms, and will be linked when it is
available.) The user could be notified through a push notification
or through Sounds and Haptics (see Section 4.1.1.3). There will be
tradeoffs between detecting potential unwanted location tracking
promptly and alerting the potential victim prematurely. One way to
handle these tradeoffs is to allow users to set the sensitivity of
these alerts. For example, the AirGuard (https://github.com/seemoo-
lab/AirGuard) app includes three different "Security Level" settings
that users can customize.
To improve the accuracy of unwanted tracking detection, a confidence
scoring mechanism can be used. Instead of issuing binary alerts for
all detected tracking devices, the system assigns a confidence score
based on multiple factors, helping distinguish between genuine
tracking threats and benign scenarios.
A confidence-based approach offers the following advantages:
* Reduced False Positives: A confidence-based approach can help
filter out benign tracking scenarios, such as transient signals or
shared family devices. Instead of triggering alerts based solely
on presence, the system can dynamically adjust its sensitivity
based on behavioral patterns. For example, if a tracking device
appears near a user only briefly or follows a predictable shared
usage pattern (e.g., a Bluetooth tag frequently used by family
members), it may be assigned a low confidence score. This
prevents unnecessary alerts while still ensuring that persistent
and anomalous tracking behaviors are flagged for user attention.
* Context-Aware Threat Evaluation: The confidence score can
incorporate contextual factors such as movement patterns, duration
of proximity, and recurrence. For instance, if a tracker is
detected only once in a public place (e.g., at a café or airport),
it is less likely to indicate malicious tracking. However, if the
same tracker reappears near the user across multiple locations or
over an extended period, its confidence score increases, prompting
a higher-priority alert.
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* Adaptive Alert Sensitivity: By dynamically adjusting detection
thresholds based on confidence scores, the system can prioritize
high-risk scenarios while minimizing unnecessary alerts. Users
may receive warnings based on escalating levels of certainty, such
as:
- Low confidence: Informational notification (e.g., "An
unfamiliar tracker was briefly detected nearby.")
- Medium confidence: Warning with recommended actions (e.g., "A
tracker has been detected multiple times near you. Check your
surroundings.")
- High confidence: Urgent alert with mitigation options (e.g., "A
tracker has been persistently following you. Consider removing
or disabling it.")
This approach ensures that users receive actionable and meaningful
alerts, reducing notification fatigue while maintaining strong
protection against unwanted tracking.
4.1.1.3. Tracking Tag Alerts
Tracking tags may be difficult to locate, and users may not have a
device that can actively or passively scan for tracking tags. The
DULT protocol should be built with accessibility in mind
(https://cdt.org/insights/centering-disability-in-mitigating-harms-
of-bluetooth-tracking-technology/) so that the most people can be
protected by the protocol. In addition to push notifications on
nearby devices, tracking tags themselves should be able to notify end
users. This should include periodic sounds when away from an owner,
along with lights and haptics so that people who are Deaf or hard of
hearing can still locate them.
4.1.2. Finding Tracking Tags
Even after a location tracker is detected through passive or active
scanning, a user may have difficulty in locating it. For example, a
tag may be buried under a vehicle cushion. Platforms should allow
users who have discovered a tracker through passive or active
scanning to request that the tracker signal its presence. This
assistance should be done in a way that is accessible to users with
sensory or other impairments by using multimodal signals as described
in Section 4.1.1.3. Platforms may also implement other methods to
assist in locating trackers, such as precision finding using Ultra-
wideband.
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4.1.3. Disabling Tracking Tags
In order to effectively prevent unwanted location tracking, users
should be able to disable location tracker tags. This includes a
non-owner user being tracked by a tag's owner, as well as an owner
user who believes that an attacker is using their own tag to track
them. Platforms should provide instructions for disabling tracking
tags once they are located.
4.1.4. Privacy and Security Requirements (TODO)
4.2. Design Constraints
There are also design constraints that the DULT Protocol must
consider, including limitations of the Bluetooth Low Energy (BLE)
protocol, power constraints, and device constraints.
4.2.1. Bluetooth constraints
Detecting trackers requires analyzing Bluetooth Low Energy (BLE)
advertisement packets. Most advertisements are publicly transmitted,
allowing passive scanning by any nearby receiver. While this enables
open detection of unknown tracking devices, it also raises privacy
concerns (see Section 1). Some BLE implementations employ randomized
MAC addresses and other privacy-preserving techniques, which could
impact persistent tracking detection.
The BLE payload in BLE 4.0 can support advertisement packets of up to
37 bytes. One current adoption of unwanted location tracking
requires 12 of these bytes for implementing the basic protocol, with
the remaining optional (see
[I-D.detecting-unwanted-location-trackers]). Implementation of the
DULT protocol will need to consider these limitations. For example,
in Eldridge et al (https://eprint.iacr.org/2023/1332.pdf),
implementing Multi-Dealer Secret Sharing required using two
advertisement packets were needed instead of one due to payload
constraints. While BLE 5.0 supports 255+ bytes of data, the protocol
is not backwards compatible and thus may not be suitable for the DULT
protocol.
BLE advertisements operate in the 2.4 GHz ISM band, making them
susceptible to interference from Wi-Fi, microwave ovens, and other
wireless devices. The presence of environmental noise may degrade
detection accuracy and introduce variability in scan results.
The BLE protocol also enforces strict power efficiency mechanisms,
such as advertising intervals and connection event scheduling, which
impact detection frequency. Devices operating in low-power modes may
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significantly reduce their advertisement frequency to conserve
energy, making periodic detection less reliable. Furthermore,
platform-level constraints, such as OS-imposed scanning limits and
background activity restrictions, further impact the consistency and
responsiveness of tracking detection mechanisms. For further
discussion of power constraints, see Section 4.2.2.
To address these challenges, detection mechanisms must balance
efficiency, privacy, and accuracy while working within the
constraints of the BLE protocol. Solutions may include leveraging
multiple observations over time, integrating probabilistic risk
scoring, and optimizing scanning strategies based on known BLE
limitations.
4.2.2. Power constraints
Unwanted tracking detection mechanisms typically rely on periodic
Bluetooth scanning to identify unknown tracking devices. However,
continuous background scanning poses a significant power challenge,
especially for mobile devices with limited battery capacity.
Maintaining high-frequency scans for extended periods can lead to
excessive energy consumption, impacting device usability and battery
longevity.
To address these concerns, detection systems must incorporate power-
efficient approaches that balance security with practicality.
Adaptive scanning strategies can dynamically adjust the scan
frequency based on contextual risk levels. For example, if a
suspicious tracking device is detected nearby, the system can
temporarily increase scan frequency while reverting to a lower-power
mode when no threats are present.
Event-triggered detection offers another alternative by activating
scanning only in specific high-risk scenarios. Users moving into a
new location or transitioning from a prolonged stationary state may
require more frequent detection, while routine movement in known safe
environments can minimize energy consumption. Additionally, passive
Bluetooth listening techniques could serve as a low-power alternative
to active scanning, allowing background detection without excessive
battery drain.
The DULT protocol must account for these power limitations in its
design, ensuring that detection mechanisms remain effective without
significantly degrading battery performance. Consideration of
device-specific constraints, such as variations in power efficiency
across smartphones, wearables, and IoT devices, will be critical in
maintaining a balance between security and usability.
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4.2.3. Device constraints
Unwanted tracking detection is constrained by the diverse range of
devices used for scanning, each with varying hardware capabilities,
operating system restrictions, processing power, and connectivity
limitations. These factors directly impact the effectiveness of
detection mechanisms and must be carefully considered in protocol
design.
Hardware variability affects detection accuracy. While newer
smartphones are equipped with advanced Bluetooth Low Energy (BLE)
chipsets capable of frequent and reliable scanning, older
smartphones, feature phones, and IoT devices may have reduced BLE
performance. Differences in antenna sensitivity, chipset power, and
OS-level access control can result in inconsistent detection, where
some devices fail to detect tracking signals as reliably as others.
Operating system restrictions can affect detection efforts,
particularly due to background Bluetooth Low Energy (BLE) scanning
policies. Both iOS and Android implement mechanisms to manage
background scanning behavior, balancing energy efficiency and privacy
considerations. On iOS, background BLE scanning operates with
periodic constraints, which may limit the frequency of detection
updates. Android applies similar policies to regulate background
processes and optimize power consumption. Additionally, privacy
frameworks on mobile platforms may influence how applications access
and process certain device-related data. These factors, along with
resource limitations in wearables and IoT devices, can impact the
feasibility of continuous scanning and detection.
Processing and memory constraints are another limiting factor,
particularly for low-end mobile devices and embedded systems.
Continuous scanning and anomaly detection algorithms, especially
those relying on machine learning-based threat detection, require
substantial processing power and RAM. Devices with limited
computational resources may struggle to maintain effective real-time
detection without degrading overall performance. Ensuring that
detection mechanisms remain lightweight and optimized for constrained
environments is essential.
Connectivity limitations introduce additional challenges. Some
unwanted tracking detection mechanisms rely on cloud-based lookups to
verify tracker identities and share threat intelligence. However,
users in offline environments, such as those in airplane mode, rural
areas with limited connectivity, or secure facilities with network
restrictions, may be unable to access these services. In such cases,
detection must rely on local scanning and offline heuristics rather
than real-time cloud-based verification.
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To address these challenges, detection mechanisms should incorporate
adaptive scanning strategies that adjust based on device
capabilities, optimizing performance while maintaining security.
Lightweight detection methods, such as event-triggered scanning and
passive Bluetooth listening, can improve efficiency on constrained
devices. Additionally, fallback mechanisms should be implemented to
provide at least partial detection functionality even when full-
featured scanning is not available. Ensuring that detection remains
effective across diverse hardware and software environments is
critical for broad user protection.
5. IANA Considerations
This document has no IANA actions.
6. Normative References
[I-D.detecting-unwanted-location-trackers]
Ledvina, B., Eddinger, Z., Detwiler, B., and S. P.
Polatkan, "Detecting Unwanted Location Trackers", Work in
Progress, Internet-Draft, draft-detecting-unwanted-
location-trackers-01, 20 December 2023,
<https://datatracker.ietf.org/doc/html/draft-detecting-
unwanted-location-trackers-01>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/rfc/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.
Acknowledgments
TODO acknowledge.
Authors' Addresses
Maggie Delano
Swarthmore College
Email: mdelano1@swarthmore.edu
Jessie Lowell
National Network to End Domestic Violence
Email: jlowell@nnedv.org
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Shailesh Prabhu
Nokia
Email: shailesh.prabhu@nokia.com
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