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January 5, 2023

BlackMatter's Smash-and-Grab Ransom Attack Incident Analysis

Stay informed on cybersecurity trends! Read about a BlackMatters ransom attack incident and Darktrace's analysis on how RESPOND could have stopped the attack.
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
The Darktrace Analyst Team
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05
Jan 2023

Only a few years ago, popular reporting announced that the days of smash-and-grab attacks were over and that a new breed of hackers were taking over with subtler, ‘low-and-slow’ tactics [1]. Although these have undoubtedly appeared, smash-and-grab have quickly become overlooked – perhaps with worrying consequences. Last year, Google saw repeated phishing campaigns using cookie theft malware and most recently, reports of hacktivists using similar techniques have been identified during the 2022 Ukraine Conflict [2 & 3]. Where did their inspiration come from? For larger APT groups such as BlackMatter, which first appeared in the summer of 2021, smash-and-grabs never went out of fashion.

This blog dissects a BlackMatter ransomware attack that hit an organization trialing Darktrace back in 2021. The case reveals what can happen when a security team does not react to high-priority alerts. 

When entire ransomware attacks can be carried out over the course of just 48 hours, there is a high risk to relying on security teams to react to detection notifications and prevent damage before the threat escalates. Although there has been hesitancy in its uptake [4], this blog also demonstrates the need for automated response solutions like Darktrace RESPOND.

The Name Game: Untangling BlackMatter, REvil, and DarkSide

Despite being a short-lived criminal organization on the surface [5], a number of parallels have now been drawn between the TTPs (Tactics, Techniques and Procedures) of the newer BlackMatter group and those of the retired REvil and DarkSide organizations [6]. 

Prior to their retirement, DarkSide and REvil were perhaps the biggest names in cyber-crime, responsible for two of last year’s most devastating ransomware attacks. Less than two weeks after the Colonial Pipeline attack, DarkSide announced it was shutting down its operation [7]. Meanwhile the FBI shutdown REvil in January 2022 after its devastating Fourth of July Kaseya attacks and a failed return in September [8]. It is now suspected that members from one or both went on to form BlackMatter.

This rebranding strategy parallels the smash-and-grab attacks these groups now increasingly employ: they make their money, and a lot of noise, and when they’re found out, they disappear before organizations or governments can pull together their threat intelligence and organize an effective response. When they return days, weeks or months later, they do so having implemented enough small changes to render themselves and their attacks unrecognizable. That is how DarkSide can become BlackMatter, and how its attacks can slip through security systems trained on previously encountered threats. 

Attack Details

In September 2021 Darktrace was monitoring a US marketing agency which became the victim of a double extortion ransomware attack that bore hallmarks of a BlackMatter operation. This began when a single domain-authenticated device joined the company’s network. This was likely a pre-infected company device being reconnected after some time offline. 

Only 15 minutes after joining, the device began SMB and ICMP scanning activities towards over 1000 different internal IPs. There was also a large spike of requests for Epmapper, which suggested an intent for RPC-based lateral movement. Although one credential was particularly prominent, multiple were used including labelled admin credentials. Given it’s unexpected nature, this recon quickly triggered a chain of DETECT/Network model breaches which ensured that Darktrace’s SOC were alerted via the Proactive Threat Notification service. Whilst SOC analysts began to triage the activity, the organization failed to act on any of the alerts they received, leaving the detected threat to take root within their digital environment. 

Shortly after, a series of C2 beaconing occurred towards an endpoint associated with Cobalt Strike [9]. This was accompanied by a range of anomalous WMI bind requests to svcctl, SecAddr and further RPC connections. These allowed the initial compromised device to quickly infect 11 other devices. With continued scanning over the next day, valuable data was soon identified. Across several transfers, 230GB of internal data was then exfiltrated from four file servers via SSH port 22. This data was then made unusable to the organization through encryption occurring via SMB Writes and Moves/Renames with the randomly generated extension ‘.qHefKSmfd’. Finally a ransom note titled ‘qHefKSmfd.README.txt’ was dropped.

This ransom note was appended with the BlackMatter ASCII logo:

Figure 1- The ASCII logo which accompanied BlackMatter’s ransom note

Although Darktrace DETECT and Cyber AI Analyst continued to provide live alerting, the actor successfully accomplished their mission.  

There are numerous reasons that an organization may fail to organize a response to a threat, (including resource shortages, out of hours attacks, and groups that simply move too fast). Without Darktrace’s RESPOND capabilities enabled, the threat actors could proceed this attack without obstacles. 

Figure 2- Cyber AI Analyst breaks down the stages of the attack [Note: this screenshot is from V5 of DETECT/Network] 

How would the attack have unfolded with RESPOND?

Armed with Darktrace’s evolving knowledge of ‘self’ for the customer’s unique digital environment, RESPOND would have activated within seconds of the first network scan, which was recognized as highly anomalous. The standard action taken here would usually involve enforcing the standard ‘pattern of life’ for the compromised device over a set time period in order to halt the anomaly while allowing the business to continue operating as normal.

RESPOND constantly re-evaluates threats as attacks unfold. Had the first stage still been successful, it would have continued to take targeted action at each corresponding stage of this attack. RESPOND models would have alerted to block the external connections to C2 servers over port 443, the outbound exfil attempts and crucially the SMB write activity over port 445 related to encryption.

As DETECT and RESPOND feed into one another, Darktrace would have continued to assess its actions as BlackMatter pivoted tactics. These actions buy back critical time for security teams that may not be in operation over the weekend, and stun the attacker into place without applying overly aggressive responses that create more problems than they solve.

Ultimately although this incident did not resolve autonomously, in response to the ransom event, Darktrace offered to enable RESPOND and set it in active mode for ransomware indicators across all client and server devices. This ensured an event like this would not occur again. 

Why does RESPOND work?

Response solutions must be accurate enough to fire only when there is a genuine threat, configurable enough to let the user stay in the driver’s seat, and intelligent enough to know the right action to take to contain only the malicious activity- without disrupting normal business operations. 

This is only possible if you can establish what ‘normal’ is for any one organization. And this is how Darktrace’s RESPOND product family ensures its actions are targeted and proportionate. By feeding off DETECT alerting which highlights subtle or large deviations across the network, cloud and SaaS, RESPOND can provide a measured response to the potential threat. This includes actions such as:

  • Enforcing the device’s ‘pattern of life’ for a given length of time 
  • Enforcing the ‘group pattern of life’ (stopping a device from doing anything its peers haven’t done in the past)
  • Blocking connections of a certain type to a certain destination
  • Logging out of a cloud account 
  • ‘Smart quarantining’ an endpoint device- maintaining access to VPNs and company’s AV solution

Conclusion 

In its report on BlackMatter [10], CISA recommended that organizations invest in network monitoring tools with the capacity to investigate anomalous activity. Picking up on unusual behavior rather than predetermined rules and signatures is an important step in fighting back against new threats. As this particular story shows, however, detection alone is not always enough. Turning on RESPOND, which takes immediate and precise action to contain threats, regardless of when and where they come in, is the best way to counter smash-and-grab attacks and protect organizations’ digital assets. There is little doubt that the threat actors behind BlackMatter will or have already returned with new names and strategies- but organizations with RESPOND will be ready for them.

Appendices

Darktrace Model Detections (in order of breach)

Those with the ‘PTN’ prefix were alerted directly to Darktrace’s 24/7 SOC team.

  • Device / ICMP Address Scan
  • Device / Suspicious SMB Scanning Activity
  • (PTN) Device / Suspicious Network Scan Activity
  • Anomalous Connection / SMB Enumeration
  • Device / Possible RPC Lateral Movement
  • Device / Active Directory Reconnaissance
  • Unusual Activity / Possible RPC Recon Activity
  • Device / Possible SMB/NTLM Reconnaissance
  • Compliance / Default Credential Usage
  • Device / New or Unusual Remote Command Execution
  • Anomalous Connection / New or Uncommon Service Control
  • Device / New or Uncommon SMB Named Pipe
  • Device / SMB Session Bruteforce
  • Device / New or Uncommon WMI Activity
  • (PTN) Device / Multiple Lateral Movement Model Breaches
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / SSL or HTTP Beacon
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Device / Anomalous SMB Followed By Multiple Model Breaches
  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Server Activity / Rare External from Server
  • Anomalous Connection / Anomalous SSL without SNI to New External
  • Anomalous Connection / Rare External SSL Self-Signed
  • Device / Long Agent Connection to New Endpoint
  • Compliance / SMB Drive Write
  • Anomalous Connection / Unusual Admin SMB Session
  • Anomalous Connection / High Volume of New or Uncommon Service Control
  • Anomalous Connection / Unusual Admin RDP Session
  • Device / Suspicious File Writes to Multiple Hidden SMB Shares
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Compliance / SSH to Rare External Destination
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / Download and Upload
  • (PTN) Unusual Activity / Enhanced Unusual External Data Transfer
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • (PTN) Compromise / Ransomware / Suspicious SMB Activity

List of IOCs 

Reference List 

[1] https://www.designnews.com/industrial-machinery/new-age-hackers-are-ditching-smash-and-grab-techniques 

[2] https://cybernews.com/cyber-war/how-do-smash-and-grab-cyberattacks-help-ukraine-in-waging-war/

[3] https://blog.google/threat-analysis-group/phishing-campaign-targets-youtube-creators-cookie-theft-malware/

[4] https://www.ukcybersecuritycouncil.org.uk/news-insights/articles/the-benefits-of-automation-to-cyber-security/

[5] https://techcrunch.com/2021/11/03/blackmatter-ransomware-shut-down/ 

[6] https://www.trellix.com/en-us/about/newsroom/stories/research/blackmatter-ransomware-analysis-the-dark-side-returns.html

[7] https://www.nytimes.com/2021/05/14/business/darkside-pipeline-hack.html

[8] https://techcrunch.com/2022/01/14/fsb-revil-ransomware/ 

[9] https://www.virustotal.com/gui/domain/georgiaonsale.com/community

[10] https://www.cisa.gov/uscert/ncas/alerts/aa21-291a

Credit to: Andras Balogh, SOC Analyst and Gabriel Few-Wiegratz, Threat Intelligence Content Production Lead

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
The Darktrace Analyst Team

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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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