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March 8, 2024

Malicious Use of Dropbox in Phishing Attacks

Understand the tactics of phishing attacks that exploit Dropbox and learn how to recognize and mitigate these emerging cybersecurity threats.
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
Ryan Traill
Content Manager - Digital Customer Success
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08
Mar 2024

Evolving Phishing Attacks

While email has long been the vector of choice for carrying out phishing attacks, threat actors, and their tactics, techniques, and procedures (TTPs), are continually adapting and evolving to keep pace with the emergence of new technologies that represent new avenues to exploit. As previously discussed by the Darktrace analyst team, several novel threats relating to the abuse of commonly used services and platforms were observed throughout 2023, including the rise of QR Code Phishing and the use of Microsoft SharePoint and Teams in phishing campaigns.

Dropbox Phishing Attacks

It should, therefore, come as no surprise that the malicious use of other popular services has gained traction in recent years, including the cloud storage platform Dropbox.

With over 700 million registered users [1], Dropbox has established itself as a leading cloud storage service celebrated for its simplicity in file storage and sharing, but in doing so it has also inadvertently opened a new avenue for threat actors to exploit. By leveraging the legitimate infrastructure of Dropbox, threat actors are able to carry out a range of malicious activities, from convincing their targets to unknowingly download malware to revealing sensitive information like login credentials.

Darktrace Detection of Dropbox Phishing Attack

Darktrace detected a malicious attempt to use Dropbox in a phishing attack in January 2024, when employees of a Darktrace customer received a seemingly innocuous email from a legitimate Dropbox address. Unbeknownst to the employees, however, a malicious link had been embedded in the contents of the email that could have led to a widespread compromise of the customer’s Software-as-a-Service (SaaS) environment. Fortunately for this customer, Darktrace / EMAIL quickly identified the suspicious emails and took immediate actions to stop them from being opened. If an email was accessed by an employee, Darktrace / IDENTITY was able to recognize any suspicious activity on the customer’s SaaS platform and bring it to the immediate detection of their security team.

Attack overview

Initial infection  

On January 25, 2024, Darktrace / EMAIL observed an internal user on a customer’s SaaS environment receiving an inbound email from ‘no-reply@dropbox[.]com’, a legitimate email address used by the Dropbox file storage service.  Around the same time 15 other employees also received the same email.

The email itself contained a link that would lead a user to a PDF file hosted on Dropbox, that was seemingly named after a partner of the organization. Although the email and the Dropbox endpoint were both legitimate, Darktrace identified that the PDF file contained a suspicious link to a domain that had never previously been seen on the customer’s environment, ‘mmv-security[.]top’.  

Darktrace understood that despite being sent from a legitimate service, the email’s initiator had never previously corresponded with anyone at the organization and therefore treated it with suspicion. This tactic, whereby a legitimate service sends an automated email using a fixed address, such as ‘no-reply@dropbox[.]com’, is often employed by threat actors attempting to convince SaaS users to follow a malicious link.

As there is very little to distinguish between malicious or benign emails from these types of services, they can often evade the detection of traditional email security tools and lead to disruptive account takeovers.

As a result of this detection, Darktrace / EMAIL immediately held the email, stopping it from landing in the employee’s inbox and ensuring the suspicious domain could not be visited. Open-source intelligence (OSINT) sources revealed that this suspicious domain was, in fact, a newly created endpoint that had been reported for links to phishing by multiple security vendors [2].

A few days later on January 29, the user received another legitimate email from ‘no-reply@dropbox[.]com’ that served as a reminder to open the previously shared PDF file. This time, however, Darktrace / EMAIL moved the email to the user’s junk file and applied a lock link action to prevent the user from directly following a potentially malicious link.

Figure 1: Anomaly indicators associated with the suspicious emails sent by ’no.reply@dropbox[.]com’, and the corresponding actions performed by Darktrace / EMAIL

Unfortunately for the customer in this case, their employee went on to open the suspicious email and follow the link to the PDF file, despite Darktrace having previously locked it.

Figure 2: Confirmation that the SaaS user read the suspicious email and followed the link to the PDF file hosted on Dropbox, despite it being junked and link locked.

Darktrace / NETWORK subsequently identified that the internal device associated with this user connected to the malicious endpoint, ‘mmv-security[.]top’, a couple of days later.

Further investigation into this suspicious domain revealed that it led to a fake Microsoft 365 login page, designed to harvest the credentials of legitimate SaaS account holders. By masquerading as a trusted organization, like Microsoft, these credential harvesters are more likely to appear trustworthy to their targets, and therefore increase the likelihood of stealing privileged SaaS account credentials.  

Figure 3: The fake Microsoft login page that the user was directed to after clicking the link in the PDF file.

Suspicious SaaS activity

In the days following the initial infection, Darktrace / IDENTITY began to observe a string of suspicious SaaS activity being performed by the now compromised Microsoft 365 account.

Beginning on January 31, Darktrace observed a number of suspicious SaaS logins from multiple unusual locations that had never previously accessed the account, including 73.95.165[.]113. Then on February 1, Darktrace detected unusual logins from the endpoints 194.32.120[.]40 and 185.192.70[.]239, both of which were associated with ExpressVPN indicating that threat actors may have been using a virtual private network (VPN) to mask their true location.

FIgure 4: Graph Showing several unusual logins from different locations observed by Darktrace/Apps on the affected SaaS account.

Interestingly, the threat actors observed during these logins appeared to use a valid multi-factor authentication (MFA) token, indicating that they had successfully bypassed the customer’s MFA policy. In this case, it appears likely that the employee had unknowingly provided the attackers with an MFA token or unintentionally approved a login verification request. By using valid tokens and meeting the necessary MFA requirements, threat actors are often able to remain undetected by traditional security tools that view MFA as the silver bullet. However, Darktrace’s anomaly-based approach to threat detection allows it to quickly identify unexpected activity on a device or SaaS account, even if it occurs with legitimate credentials and successfully passed authentication requirements, and bring it to the attention of the customer’s security team.

Shortly after, Darktrace observed an additional login to the SaaS account from another unusual location, 87.117.225[.]155, this time seemingly using the HideMyAss (HMA) VPN service. Following this unusual login, the actor was seen creating a new email rule on the compromised Outlook account. The new rule, named ‘….’, was intended to immediately move any emails from the organization’s accounts team directly to the ‘Conversation History’ mailbox folder. This is a tactic often employed by threat actors during phishing campaigns to ensure that their malicious emails (and potential responses to them) are automatically moved to less commonly visited mailbox folders in order to remain undetected on target networks. Furthermore, by giving this new email rule a generic name, like ‘….’ it is less likely to draw the attention of the legitimate account holder or the organizations security team.

Following this, Darktrace / EMAIL observed the actor sending updated versions of emails that had previously been sent by the legitimate account holder, with subject lines containing language like “Incorrect contract” and “Requires Urgent Review”, likely in an attempt to illicit some kind of follow-up action from the intended recipient.  This likely represented threat actors using the compromised account to send further malicious emails to the organization’s accounts team in order to infect additional accounts across the customer’s SaaS environment.

Unfortunately, Darktrace's Autonomous Response was not deployed in the customer’s SaaS environment in this instance, meaning that the aforementioned malicious activity did not lead to any mitigative actions to contain the compromise. Had RESPOND been enabled in autonomous response mode at the time of the attack, it would have quickly moved to log out and disable the suspicious actor as soon as they had logged into the SaaS environment from an unusual location, effectively shutting down this account takeover attempt at the earliest opportunity.

Nevertheless, Darktrace / EMAIL's swift identification and response to the suspicious phishing emails, coupled with Darktrace / IDENTITY's detection of the unusual SaaS activity, allowed the customer’s security team to quickly identify the offending SaaS actor and take the account offline before the attack could escalate further

Conclusion

As organizations across the world continue to adopt third-party solutions like Dropbox into their day-to-day business operations, threat actors will, in turn, continue to seek ways to exploit these and add them to their arsenal. As illustrated in this example, it is relatively simple for attackers to abuse these legitimate services for malicious purposes, all while evading detection by endpoint users and security teams alike.

By leveraging these commonly used platforms, malicious actors are able to carry out disruptive cyber-attacks, like phishing campaigns, by taking advantage of legitimate, and seemingly trustworthy, infrastructure to host malicious files or links, rather than relying on their own infrastructure. While this tactic may bypass traditional security measures, Darktrace’s Self-Learning AI enables it to recognize unusual senders within an organization’s email environment, even if the email itself seems to have come from a legitimate source, and prevent them from landing in the target inbox. In the event that a SaaS account does become compromised, Darktrace is able to identify unusual login locations and suspicious SaaS activities and bring them to the attention of the customer for remediation.

In addition to the prompt identification of emerging threats, Darktrace's Autonomous Response is uniquely placed to take swift autonomous action against any suspicious activity detected within a customer’s SaaS environment, effectively containing any account takeover attempts in the first instance.

Credit to Ryan Traill, Threat Content Lead, Emily Megan Lim, Cyber Security Analyst

Appendices

Darktrace Model Detections  

- Model Breach: SaaS / Access::Unusual External Source for SaaS Credential Use

- Model Breach: SaaS / Unusual Activity::Multiple Unusual External Sources For SaaS Credential

- Model Breach: SaaS / Access::Unusual External Source for SaaS Credential Use

- Model Breach: SaaS / Access::Unusual External Source for SaaS Credential Use

- Model Breach: SaaS / Unusual Activity::Multiple Unusual SaaS Activities

- Model Breach: SaaS / Unusual Activity::Unusual MFA Auth and SaaS Activity

- Model Breach: SaaS / Compromise::Unusual Login and New Email Rule

- Model Breach: SaaS / Compliance::Anomalous New Email Rule

- Model Breach: SaaS / Compliance::New Email Rule

- Model Breach: SaaS / Compromise::SaaS Anomaly Following Anomalous Login

- Model Breach: Device / Suspicious Domain

List of Indicators of Compromise (IoCs)

Domain IoC

mmv-security[.]top’ - Credential Harvesting Endpoint

IP Address

73.95.165[.]113 - Unusual Login Endpoint

194.32.120[.]40 - Unusual Login Endpoint

87.117.225[.]155 - Unusual Login Endpoint

MITRE ATT&CK Mapping

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS

T1078.004 - Cloud Accounts

DISCOVERY

T1538 - Cloud Service Dashboard

RESOURCE DEVELOPMENT

T1586 - Compromise Accounts

CREDENTIAL ACCESS

T1539 - Steal Web Session Cookie

PERSISTENCE

T1137 - Outlook Rules

INITIAL ACCESS

T156.002 Spearphishing Link

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
Ryan Traill
Content Manager - Digital Customer Success

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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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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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Andrew Hollister
Principal Solutions Engineer, Cyber Technician
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