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August 2, 2024

Safelink Smuggling: Enhancing Resilience Against Malicious Links

Gain insights into safelink smuggling tactics and learn strategies to protect your organization from the dangers posed by malicious links.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Stephen Pickman
Senior Vice President, Engineering
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02
Aug 2024

Darktrace security members and researchers have recently seen a rise in what we are calling Safelink Smuggling. Safelinks are URLs rewritten by security solutions to enable additional analysis when the URL is clicked. Once analyzed, they may prompt a user, redirect the browser back to the original URL, or block further access if deemed necessary.

What is Safelink Smuggling?

Safelink Smuggling is a technique that involves an attacker purposely getting their malicious payload rewritten by a security solution’s Safelink capability to then propagate the rewritten URL to others. This technique is a way for attackers to not only avoid detection by traditional email security and other solutions, but also to instill mistrust in all email security solutions. As a result, Safelinks from a range of popular email security providers are often seen in phishing or supply chain attacks. In fact, Darktrace has observed over 300,000 cases of Safelinks being included in unexpected and suspicious contexts over the last 3 months.

How does Safelink Smuggling work?

Safelink Smuggling has two key stages: Getting a malicious link rewritten by an email security solution, then propagating that rewritten link to other victims.

Step one:

Obfuscated a malicious payload through a Safelink capability rewriting the link; Darktrace has seen this attempted through two methods – Compromised Account or Reply-Chain.

  • Method 1: Compromised Account

If an attacker can gain access to a compromised account – whether that’s through brute force, malware or credential theft – they can infiltrate it with malicious links, and then exfiltrate the Safelinks created as the email passes through security filtering. In other words, attackers will send a malicious payload to the compromised inbox, with the intent that the malicious URL gets rewritten. Unlike a normal phishing email where the threat actor wants to avoid having their email blocked, in this case the objective is for the email to get through to the inbox with the link rewritten. As observed by Darktrace, attackers often send the link in isolation as any additional components (i.e., body text or other content in the email) could cause a more severe action such as the email security solution holding the message.

  • Method 2: Reply-Chain

With this method, the attacker sends a malicious link to an email security vendor’s customer in an attempt to solicit a reply from an internal user. This allows them to grab the re-written URL within the reply chain. However, this is a risky tactic which can fail at several points. The attacker has to be confident the initial email won't be blocked outright; they also risk alerting security vendors to the address and the URL intended to be used for the main campaign. They also must be confident that the checks made when the re-written URL is clicked will not lead to a block at the final destination.
Regardless of the method used, the end result will appear as follows:

For example, the original malicious URL may look like this,

faceldu[.]org/Invoice112.zip

(negative surface indicators: recently registered domain, file extension)

And after being rewritten,

securityvevndor[.]com/safe?q=aNDF80dfaAkAH930adbd

(positive surface indicators: established domain, positive reputation, associated with safe content)

Step Two:

Now that the attacker has access to a malicious URL that has been obfuscated by a safe rewrite, attackers can forward or craft an email leveraging that same link. In fact, we have even seen multiple layers of Safelink Smuggling being used to mask a payload further.

The Challenge of Link Rewriting

Traditional email security solutions rewrite all links sent to an organization, but there is an inherent risk to this methodology. Rewriting every link, whether harmless or harmful, leads employees to lose context and creates a false sense of security when interacting with rewritten links in emails. Furthermore, it provides attackers with many opportunities to exploit Safelinks. As demonstrated in Method 2 above, if an email security solution does not rewrite every link, executing such attacks would be significantly more challenging.

Traditionally, rewriting every link made sense from a security perspective, as it allowed servers to thoroughly analyze links for known attack patterns and signatures. However, this approach relies on identifying previously recognized threats. Conversely, Darktrace / EMAIL gathers sufficient information about a link without needing to rewrite it, by analyzing the context and content of the email and the link itself.

In fact, Darktrace is the pioneer in applying selective rewriting to URLs based on suspicious properties or context, a method that other solutions have since adopted. While traditional solutions rewrite links to assess them only after they are clicked, Darktrace / EMAIL takes immediate action to neutralize threats before they reach the inbox.

Darktrace achieves high success rates in detecting malicious links and emails on the first encounter using Self-Learning AI. By understanding 'normal' behavior in email communications, Darktrace identifies subtle deviations indicative of cyber threats and selectively rewrites only those links deemed suspicious, ensuring a targeted, proportionate, and non-disruptive response.

Why do traditional email security solutions miss Safelink attacks?

Traditional security solutions that focus on learning attack patterns will miss Safelink threats as they are often utilized in attacks that have a variety of layers which help the email seem legitimate. Leveraging all the classic techniques seen in a supply chain attack to disguise the sender's intent, taking advantage of the users' inherent trust in familiar sources, the user is more likely to lower their defenses.

For more information: https://darktrace.com/products/email/use-cases/supply-chain-attack

In terms of the URL, if the payload is malicious, why is it difficult for email security solutions to catch it? Primarily, other security vendors will focus on the payload in isolation, attempting to find known attack patterns or signatures such as a domain name or IP with a bad reputation. Unfortunately, with this technique, if the URL has a legitimate domain, it will return a clean track record. Common obfuscation techniques such as captchas, short-links, and click throughs can all be deployed to add layers of complexity to the analysis.

Safelink Smuggling relies heavily on link redirects, which means that web analysis tools will falter as they will only analyze the first redirect. Consequently, when more in-depth analysis on the link itself is performed, the first place the URL takes the user is not the malicious site but rather the default on-click analysis of the vendor in question. Therefore, any traditional browser or link analysis will also return a negative result.

Finally, the context itself is important. In contrast to traditional email security solutions, Darktrace / EMAIL asks who, what, when, where, and why for every single email, and compares it to the pattern of life of both the internal recipient and the external sender, rather than attempting to match patterns with historical threat data. When analyzing an email from an inbound perspective, Darktrace reveals potential deviations from normal, that, when considered sufficiently anomalous, will result in taking a proportional action to the threat assessed.

To illustrate the above, let’s take a look at an example email that Darktrace recently caught.

The following is an email a Darktrace customer received, which Darktrace / EMAIL held before it reached the inbox. In this case, the smuggled Safelink was further obfuscated behind a QR Code. The accompanying document also presented some anomalies in terms of its intent, perceived as a potential social engineering attempt. Finally, the lack of association and low mailing history meant there was no prior context for this email.  

Example of a Safelink Smuggling attack using a popular email security solution’s safelink.
Fig 1: Example of a Safelink Smuggling attack using a popular email security solution’s safelink.

How to mitigate against Safelink Smuggling?

It's difficult for email security vendors to do anything about their links being reused, and reuse should almost be expected by popular operators in the email security space. Therefore, the presence of links from a vendor’s domain in a suspicious email communication rarely indicates a compromise of the link rewrite infrastructure or a compromise of the third-party vendor.

Email security vendors can improve their defense-in-depth, especially around their email provider accounts to avoid Method 1 (Compromised Account attacks) and become more selective with their rewrites to curtail Method 2 (Reply Chain attacks).

Primary protection against Safelink Smuggling should be offered by the email security vendor responsible for inbound email analysis. They need to ensure that techniques such as Safelink Smuggling are not evaded by their detection mechanisms.

Darktrace has long been working on the betterment of security within the email community and innovating our link analysis infrastructure to mitigate against this attack methodology (read more about our major update in 6.2 here), regardless of whether the receiving organization are Darktrace customers.

How does Darktrace deal with Safelink Smuggling today?

Darktrace has been dealing with Safelink Smuggling since launch and has a standardized recommendation for customers who are looking to defend against this threat.

Customers want to avoid being 1) the propagators of this threat and potentially damaging their brand reputation, and 2) being victims of the supply chain attack thereafter.

The principal recommendation to protect customer accounts and consequently their brands is to ensure defense-in-depth. As accounts establish themselves as the crown jewels of any modern enterprise, organizations should vigilantly monitor their account activity with the same rigor they would analyze their network activity. Whether that is through the base account takeover protection offered by Darktrace / EMAIL, or the expanded defense offered by Darktrace / IDENTITY, it is crucial that the accounts themselves have a robust security solution in place.

Secondly, to avoid falling victim to the supply chain attack that leverages a third-party vendor’s link rewrite, it is imperative to use a solution that does not rely on static threat intelligence and link reputation analysis. Rather than chasing attackers by updating rules and signatures, Darktrace leverages Self-Learning AI to learn the communication patterns of both internal and external messages to reveal deviations in both content and context.

Finally, for those customers that already leverage Darktrace / EMAIL we recommend ensuring that lock links are enabled, and that the default warning page is displayed every time a link is rewritten, no matter the perceived severity of the link. This will allow any potential user that clicks on a rewritten Darktrace / EMAIL link to be alerted to the potential nature of the site they are trying to access.

Safelink smuggling example caught by Darktrace

While most cases involve other vendors, analysts recently saw a case where Darktrace's own links were used in this type of attack. A small number of links were leveraged in a campaign targeting both Darktrace and non-Darktrace customers alike. Thankfully, these attempts were all appropriately actioned by those customers that had Darktrace / EMAIL deployed.

In the example below, you will see how Darktrace Cyber AI Analyst describes the example at hand under the Anomaly Indicators section.

Example of Safelink Smuggling attack on Darktrace using the Darktrace Safelink Infrastructure.
Fig 2: Example of Safelink Smuggling attack on Darktrace using the Darktrace Safelink Infrastructure.

First, the display name mismatch can be interpreted as an indicator of social engineering, attempting to deceive the recipient with an IT policy change.

Second, the link itself, which in this case is a hidden redirect to an unusual host for this environment.

Finally, there is a suspected account takeover due to the origin of the email being a long-standing, validated domain that contains a wide variety of suspicious elements.

Darktrace / EMAIL would have held this email from being delivered.

Conclusion

By investigating Safelink Smuggling, Darktrace wants to shine a light on the technique for security teams and help raise awareness of how it can be used to dupe users into lowering their defenses. Challenge your email security vendor on how it deals with link analysis, particularly from trusted senders and applications.

Interested in Darktrace’s approach to defense-in-depth? Check out Darktrace / EMAIL

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Stephen Pickman
Senior Vice President, Engineering

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July 7, 2026

Securing AI: Analysis of the Complete Security Stack with Governance and Controls

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Why traditional cybersecurity approaches are not enough for AI

AI adoption outpaces most security programs’ ability to adapt.  That gap is now one of the most consequential sources of cyber risk facing enterprises. As organizations embed generative and agentic AI into development workflows, business operations, and security tooling itself, the question is no longer whether AI will introduce risk. The question is whether organizations understand where that risk actually lives and how to manage it operationally.  

Two recent pieces of guidance underscore this shift:

  1. The upcoming Cybersecurity Framework Profile for AI from NIST
  1. The Five Eyes government guidance on the careful adoption of agentic AI services

Taken together, they point to a critical conclusion. AI security cannot be reduced to model hardening or prompt filtering. It requires a defense in depth strategy that treats AI as both a new attack surface and a force multiplier for defense, while accounting for how AI fundamentally changes scale, speed, and autonomy.  

Recent threat research suggests that today's cyber risk is driven less by initial compromise and more by an adversary's ability to blend into normal operations over time. AI systems create the same exposure in a new form: more autonomy, more scale, and more opportunities for risky behavior to blend into normal operations.

How NIST defines the three core pillars of AI security

The NIST profile organizes AI risk across three inseparable focus areas that span all cybersecurity functions, Secure, Defend and Thwart. These areas are not sequential. They exist simultaneously and must be addressed together.

Secure

This treats AI as an attack surface. It includes models, prompts, agents, pipelines, training and inference data, retrieval augmented generation corpora, and the AI supply chain itself. AI systems are opaque, probabilistic, and non-deterministic by design. Some vulnerabilities are inherent in how models are trained or how data is sourced. Traditional patching does not fully mitigate these risks. This is also where many enterprises are weakest today and, critically, where many security programs stop.  

Defend

This is AI as a defensive force multiplier. AI can improve detection speed, scale, correlation, and response, but only if the right models are used and operationalized correctly. Machine-speed behavior-based detection, response and containment becomes critical in defending non-deterministic systems. Accuracy, explainability, governance, testing, validation, and integration into SOC workflows matter as much as capability. Without those controls, hallucination risk, over automation, and misplaced trust become security risks themselves.  

Thwart

This treats AI as an adversarial accelerant. Threat actors are already using AI to generate targeted social engineering attacks, deepfakes, malware, and autonomous attack agents. Asymmetric warfare is highlighting faster vulnerability discovery and exploitation with a lag on patch development, testing and deployment.  

How this looks in practice

Darktrace researchers observed scaled, automated exploitation of the React2Shell vulnerability within days of disclosure. A vulnerable cloud asset was exploited in under 120 seconds of being deployed. Darktrace research team observed an AI/LLM-generated malware sample used in exploitation activity tied to React2Shell. The significance isn't novelty. It is that AI lowers the barrier to producing usable offensive tooling and compresses the time between experimentation and deployment.  

Tactics are getting more and more creative in order to string together steps of an attack kill chain. This creates a dependency on behavior-based detection, autonomous investigation, autonomous containment, training, resilience investment, and recovery planning across the entire enterprise.

Why agentic AI fundamentally changes enterprise cyber risk

The Five Eyes guidance on agentic AI highlights material changes to the cyber risk profile of an organization. Unlike generative AI systems that produce content for human consumption, agentic AI systems reason, plan, and act autonomously across tools, data, and environments. That autonomy, combined with access to real systems, amplifies the impact of traditional cyber failures and introduces new system level risks that are difficult to predict, observe, and contain.  

Risk in agentic systems does not live in the model alone. It emerges from interactions between models, prompts, memory, tools, APIs, identities, privileges, inter-agent trust relationships, and human assumptions baked into design. Vulnerabilities are often introduced through data, connectors, natural language interfaces, protocols, and drift by design.

In supply-chain incidents, attackers did not need sophisticated exploits to scale impact. They abused trusted systems built for automation and implicit access. Agentic AI inherits that model. Once a system can act across tools, data, and workflows, compromise propagates through trust relationships that were never designed for machine autonomy.

The major agentic AI risk classes include the following:  

  • The identity control for non-human identities or autonomous agents makes it difficult to mitigate over-permissioning, limiting access, scope, and duration, as well as access hygiene
  • Agents are frequently over permissioned
  • Compromised tools inherit agent authority
  • Static secrets enable impersonation
  • Implicit trust between agents enables lateral movement

Design and configuration risks compound this, including privileges evaluated once at startup, poor segmentation, unvetted third party tools, reused authorization decisions outside their original context, and guardrail limitations.  

Behavioral risk  

Agents can optimize for goals in unsafe ways, misinterpret ambiguous intent, chain actions into unintended sequences, change behavior during evaluation, and exhibit deceptive or sycophantic responses.  

Structural risk  

Structural risk follows from agentic systems that are tightly coupled, multicomponent ecosystems. Failures can propagate across agents. Hallucinations cascade downstream. Resource exhaustion becomes systemic. Tool misuse enables indirect prompt injection and command execution. Rogue agents can poison peer agents through trust relationships.  

Accountability

Accountability becomes unclear as autonomy increases. Autonomous agents assume human identity permissions, and humans should have clear ownership of these agents, but they don’t, and this model is flawed. Decision paths are opaque and non-deterministic. Logs are fragmented and difficult to interpret. Reproducing an incident will be impossible without explicit design for observability and forensics. An agent compromise is functionally an insider threat, often with better access and fewer behavioral constraints than a human.  

What does defense in depth look like for AI?

Agentic AI runs on software, networks, identities, and data. It must be governed using the same foundational principles that have proven resilient under uncertainty, including secure by design, defense in depth, zero trust, least privilege, continuous monitoring, behavior-based advanced threat detection and containment, and incident response and recovery.

Core components to a Defense in depth Strategy for Securing the use of AI:

  • Strong, precise identity control plane to include an identity per agent (cryptographic, non‑shared)
    • Privilege monitoring and just‑in‑time access
  • Data Governance
  • Secure‑by‑default configurations
    • Security Posture Management  
    • Zero Trust principles  
  • Strong guardrails, deny‑by‑default policies, and isolation
  • Explicit instruction hierarchies and controlled context
  • Behavioral-based detection across entire enterprise to include inputs, tools, and outputs as well as AI used on the endpoint, across the network, cloud, SaaS, email, and OT
    • Runtime anomaly detection and goal‑drift detection
    • Autonomous containment to mitigate risk and minimize damage
  • Hard boundaries on autonomy and delegation
  • Testing, Evaluation, Validation and Verification  
    • Determine when autonomous action and when human in the loop
    • Adversarial training and agent‑specific testing
    • Simulation, red teaming, and chaos testing
  • Kill‑switches, rollback, and containment mechanisms
    • Forensics data captures, interpretability, autonomous containment, and remediation/recovery plans  

Until standards, tooling, and assurance methods mature, organizations should assume agentic AI systems will behave unexpectedly and design deployments around resilience, behavior-based detection, reversibility, and containment, not efficiency.

How security leaders should prepare for enterprise AI adoption

AI security is not model security alone. Data, pipelines, identities, and agents are first class assets. Many AI attacks succeed through standard cyber failures amplified by AI. Identity, data, and supply chain risk dominate. Behavior-based detection and response are critical, not optional. Logging, provenance, versioning, and forensics data capture of detections are mandatory because you cannot investigate or recover from AI incidents without them.  

Risk will often be visible in behavior before it is clearly defined in policy or guidance. The same pattern has been seen in pre-CVE disclosure detection, where abnormal activity appears before the industry has named or described the vulnerability. AI systems introduce that uncertainty by design.

Security leaders should prioritize controls before AI is fully deployed, avoid generic AI security checklists, integrate AI risk into existing cyber programs, and mitigate the risk of non-deterministic technology with continuous oversight, monitoring, behavior analytics, anomaly detection, autonomous investigation, and autonomous containment.

Visibility has a different connotation with AI. Previously, audit logging worked for software/people, but with Generative AI-based systems, interpretability and explainability is difficult to understand, you cannot "undo" what has been done, or see the logic or control a chain of events. This is why behavioral-based detections and containment becomes critical.  

What capabilities should every AI security program include?

If an organization asked “what must be in place before scaling AI?”:

  1. AI Risk board and approval workflow
  1. IAM + PAM for all AI services and agents
  1. AI asset inventory
  1. Prompt/output DLP with sanctioned AI access – This is not just pre- and post- filters, but behavior-based detections of semantic interface as well as behavior-based analysis of output with associated risk context.  
  1. Shadow AI identification
  1. Secure MLOps – This is an entire paper itself
  1. Runtime guardrails and tool restrictions
    • Including AI Gateway/SASE/Zero trust/
  1. Runtime security with behavior-based detections
    • Complete visibility, monitoring, behavior analytics, anomaly detection, risk/intent/context evaluation of anomalies, autonomous investigation and autonomous containment of all AI assets across endpoint, network, SaaS, SASE, cloud, OT, email, and messaging platforms
  1. Secure data pipelines and data governance
  1. SOC workflow changes from malicious classification workflows to behavior-based detection workflows
  1. Remediation plans for AI-related incidents  

Layered Governance and Security Stack for Securing AI  

The following outline considers governance and security tools that should be considered, well-integrated, deployed, tested, operationalized and embedded within security workflows. These tools and controls map to NIST’s CMF for AI.  

These considerations do not need to be implemented in order. Runtime Detect and Respond will help mitigate risk while Governance, Visibility, and Identity mature.

Category Tooling Controls
Governance & Visibility
  • AI asset inventory / AI CMDB
  • Shadow AI discovery
  • SaaS discovery
  • AI usage on non-endpoint managed systems via network or cloud telemetry
  • MCP server/client usage via protocols
  • Browser telemetry
  • Gateway or SASE telemetry
  • Establish a risk board to set up controls
  • Mandatory registration of AI systems
  • Owner, data classification, intended use, and risk tier
  • Supplier disclosure requirements
  • Risk mitigation plan for AI adoption, innovation, or development
Identity, Access & Agent Control

Non-human autonomous agents should not have the full permissions associated with a human user.

  • IAM with workload identities
  • PAM for AI service accounts
  • Secrets management with short-lived tokens
  • Zero Trust principles
  • Identity, permission, and token hygiene
  • Unique identities per model, agent, and pipeline
  • Least privilege for tools, data, and APIs
  • Explicit approval for autonomous actions
Data Security & Privacy
  • Data classification and labeling
  • Enterprise DLP across endpoint, email, network, cloud, and SaaS
  • Forensics data capture after risky detections
  • Prompt-level DLP through behavior-based semantic analysis with risk and intent context
  • Input/interface analysis for risky data requests
  • Output analysis for sensitive data
  • Data integrity evaluation
  • Retention and redaction policies for prompts and responses
Secure MLOps / LLMOps
  • Secure CI/CD with AI-specific gates
  • Model registries with approval workflows
  • Dependency, container, and artifact scanning
  • SBOM/AIBOM generation
  • IaC security scanning
  • Security posture management
  • Misconfiguration identification
  • Hardening recommendations
  • Signed models and prompts
  • Versioned datasets, configurations, logging, and controls
  • Securing data pipelines
  • Controlled promotion
  • Quality assurance
  • Adversarial testing
Runtime Security

Securing runtime goes beyond guardrails and model firewalls to include behavior-based detections, response, and containment.

  • Detection, monitoring, and SOC integration
  • Centralized visibility into prompts, outputs, and tool calls
  • AI-specific detections
  • Behavior-based detection for AI usage patterns
  • Model drift and behavior monitoring
  • Autonomous containment
  • Behavior-based detection of model inputs and outputs
  • Prompt injection detection
  • Model manipulation, including jailbreaking, poisoning, and related attacks
  • Sensitive data access attempts
  • Behavior-based detection across low-code agents, high-code agents, MCP clients and servers, endpoint, network, cloud, email, SaaS, SASE, IoT, and OT
  • Policy enforcement between users, models, tools, agents, SaaS models/tools, and MCP servers/clients
  • Risk, intent, and context evaluation for detections and response actions
Response & Recovery
  • Autonomous containment
  • AI-assisted playbooks
  • Forensics data capture for AI-related events
  • Model rollback mechanisms
  • Backup and restore for models and datasets
  • Kill switch for agents
  • Autonomous response to agents performing risky behaviors
  • Model and dataset rollback
  • Remediation plans
  • Tabletop exercises
  • Supplier coordination plans
  • Post-incident AI performance validation

AI security requires continuous visibility and behavioral detection

AI changes how fast systems move, how decisions are made, and how risk propagates. It does not change the fundamentals of security. Organizations that succeed will be the ones that apply those fundamentals rigorously, assume failure, and build systems that can detect, contain, and recover when AI behaves in ways they did not anticipate. Security is not what AI is allowed to do. It is whether the organization can understand, trust, and control what AI actually does in practice.  

Take this guidance to understand different initiatives that organizations should be considering. Securing AI is the most critical component to AI safety. As organizations invest more in AI adoption, they should be investing in security in order to mitigate the risk of AI adoption. Organizations should be evaluating their governance and security stack to include well-integrated tools that are deployed, tested, operationalized and embedded within security workflows. While organizations mature in governance, visibility and identity access management, they should be investing in behavior-based detection and autonomous containment to mitigate AI risk.  

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About the author
Nicole Carignan
SVP, Security & AI Strategy, Field CISO

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July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

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Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician
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