Gaps in Traditional DFIR Playbooks: Machine Learning Models

Incidents involving malicious ML models reveal significant weaknesses in standard Digital Forensics and Incident Response (DFIR) procedures, which are traditionally focused on executable malware, scripts, or phishing-based vectors. When the “malware” is a machine learning artifact—such as a .pt or .pkl file—existing tools, training, and playbooks often fall short.

Lack of Recognition of ML Artifacts as Threat Vectors

DFIR playbooks rarely consider ML model files as potential root causes of compromise. Investigators may misattribute post-compromise activity to benign developer behavior, unaware that loading a model file may have silently triggered the incident. Frameworks like MITRE ATT&CK lack explicit coverage of ML threats, and the more relevant MITRE ATLAS remains largely theoretical.

Inadequate Scanning & EDR Coverage

EDR/XDR tools typically treat model files as inert blobs and fail to analyze embedded serialization logic or hidden payloads. Heuristic scanners like Hugging Face’s PickleScan are limited and can be bypassed with simple evasion techniques such as compression or opcode manipulation. This results in dangerous false negatives and missed threats.

Limited Expertise and Playbook Procedures

Most IR teams lack the technical familiarity to safely dissect ML model files or understand their execution context. Standard procedures do not account for the possibility of code executing via model deserialization. Moreover, many teams do not have formalized workflows for engaging with data scientists—whose insights are critical for understanding the operational context of ML artifacts.

Forensic Tooling Gaps

Traditional forensic tools are optimized for executables and documents, not serialized ML models. Tools like Fickling or HiddenLayer’s scanners are not yet standard in most DFIR toolkits. This forces teams to create ad-hoc scripts or manually parse pickle files—introducing delays and risk of oversight.

Sparse “Ground Truth” and Case Studies

There is a scarcity of public case studies, threat reports, and ground-truth incident data for model-based attacks. This hampers the ability to recognize patterns or apply prior knowledge to new cases. The legal and evidentiary handling of ML models as malware is also largely untested.

Specialized IR Procedures for ML Attack Vectors

To effectively respond to AI-native threats, incident response firms must evolve beyond traditional methods and build specialized capabilities for investigating malicious machine learning (ML) artifacts. Below are key areas where firms like Security Joes can differentiate and lead:

Proactive Model Scanning in Investigations

Integrate AI model scanning into standard IR workflows. Use tools like HiddenLayer’s Model Scanner or Protect AI’s Guardian to detect malicious content in .pt, .pkl, ONNX, and other model formats. Maintain a repository of YARA rules and IoCs specific to ML threats to ensure no model file is overlooked during an investigation.

Custom Tools for Model Analysis

Develop in-house utilities to disassemble and analyze serialized models (e.g., using Fickling or Kaitai Struct). Enable safe inspection and behavior observation via sandboxed, instrumented environments tailored to ML frameworks, allowing teams to “detonate” suspect models and capture forensic evidence.

Memory Analysis Playbooks for ML Processes

Build playbook procedures for memory analysis of processes like Python or Java. Use customized Volatility plugins to detect deserialization artifacts, injected bytecode, and runtime anomalies. Treat memory capture as essential in cases involving ML-related compromise.

ML Environment Logging & Monitoring

Encourage clients to enable detailed logging (e.g., torch.load() events) in ML workflows. During investigations, analyze logs from cloud storage, package managers, or infrastructure to trace model provenance and execution—critical for attribution and scoping.

Collaboration with Data Science Teams

Engage ML engineers and data scientists as part of the IR process. These stakeholders provide crucial context about model sourcing, behavior, and pipeline integration, helping teams quickly identify suspicious activity or deviations from normal operations.

Attack Path Mapping in ML Workflows

Treat the execution of a model as the start of a potential kill chain. Investigate downstream actions—OS commands, lateral movement, data exfiltration—and build playbooks that follow the full lifecycle of an ML-based intrusion, not just the initial payload.

Use of Threat Intelligence and Attribution Techniques

An advanced IR firm will tie in threat intel to their analysis of malicious models. For example, if they extract an IP or domain from a model’s payload, they immediately cross-check it with threat intel databases to see if it’s linked to known campaigns or actors. In HiddenLayer’s analysis, the beacon’s IP was linked to groups like Nobelium/APT29​ – a skilled IR team would note that and consider the possibility of a state-sponsored adversary.

Containment and Eradication Strategies

When the root cause is a model file, containment might mean pausing AI workloads or redirecting them to known-good models. An IR plan could involve quickly replacing compromised models with clean versions or switching to safer formats (if available) to keep the business running while neutering the threat.

By developing these specialized procedures and tools, an IR firm can effectively investigate ML attack vectors with confidence. The key is being prepared and fluent in both cybersecurity and ML systems – knowing how to pull apart a model file like it’s malware, how to instrument an ML environment, and how to communicate with the teams that manage AI assets. In doing so, the firm drastically reduces the time to pinpoint a malicious model and increases the thoroughness of response, closing the unusual “back doors” that ML models can introduce.

Building Timelines and Attribution for Model-Origin Breaches

When an incident originates from a model execution, building a detailed timeline and performing attribution require some additional techniques beyond the normal IR repertoire. Here’s how investigators can piece together the chain of events and identify who/what was behind it:

Chain-of-Custody Analysis of the Model

Tracing the origin of the malicious model is vital. This involves answering: Where did the model come from, and how did it get into our environment? Responders should work backwards from the point of execution. Check package manager logs (pip install, conda logs) to see if the model came via a dependency. Check browser history or git history if someone downloaded the file manually. If the model was obtained from Hugging Face or another hub, use any available metadata (the model’s URL, author, upload date).

By establishing exactly when and by whom the model file was introduced, the timeline can mark the initial intrusion point. Often this will align with an employee’s action (like downloading a model for a project), which helps with security awareness post-incident (“next time, verify models before use or stick to trusted sources”).

Execution Timeline and Lateral Movements

Once the model was present, map out the moment of execution and what happened immediately after. The timeline might show: “10:32am – Data scientist executes script that loads model model.pkl. 10:33am – Malicious code inside model runs (this might not log an event, but we infer it). 10:33:30am – Python process spawns a new process / opens network connection / etc. 10:34am – Attacker establishes persistence or begins data exfiltration,” and so on.* Every post-infection event should be correlated with that initial model execution. This can be done by analyzing process start times (child processes of the Python interpreter), file modification times (if the payload dropped files or modified system settings), and network logs (connections starting at that minute). Many IR tools can create a timeline of system activity; the twist here is to anchor it to the model’s load time. The end result should clearly show a chain: model load -> code exec -> subsequent malicious actions. This not only confirms causation (as opposed to coincidence) but also helps scope the incident (everything after the model load on that machine could be tainted). If multiple machines loaded the same model (e.g., a developer’s workstation and a production server), create parallel timelines and then merge them to see the full picture of the attack across the org.

Subprocess and System Call Correlation

Model-based malware might not always launch new processes; it could do a lot within the running process (especially in-memory attacks). However, if it does launch even one subprocess (like a shell or a system utility), that’s incredibly useful for IR. Correlating those events means linking the parent (the ML process) to the child (malicious subprocess). EDR tools usually record parent-child relationships, so investigators should pull those records for the timeframe of interest. If, say, python.exe (running a training script) spawned cmd.exe with strange arguments, that is a clear indicator. Additionally, correlation can extend to system calls: if no new process was spawned, what system calls did the ML process make? Perhaps it opened a socket to an IP or wrote to a file. By inspecting low-level telemetry (via Sysmon logs, strace outputs, or EDR sensors), IR can attribute those actions to the model payload. For example, in the a crypto-mining case, the malicious code invoked os.chmod and subprocess.Popen to run a miner​. Identifying those function calls on the timeline (maybe via Python auditing hooks or strace logs) would pinpoint what the model’s code did at each step.

Opcode and Function Tracing (Deep Dive)

In cases involving ONNX or other format exploits, a different approach is needed. ONNX models are executed by a runtime that processes a graph of operations (opcodes). If an attacker exploited an ONNX runtime vulnerability, investigators might need to reproduce the crash or exploit in a lab to see which part of the model caused it. This is somewhat analogous to debugging a program. By running the ONNX model with instrumentation (like a debugger or with verbose logging enabled in the runtime), IR can observe if a particular operator or input causes a buffer overflow or anomalous behavior. Suppose an ONNX model had an operator with an extremely large dimension that triggers memory corruption – the forensic timeline would include the crash dump analysis (time of crash, process memory showing rogue shellcode). To attribute this, one might match the shellcode or method to known exploits (for instance, if a known exploit for ONNX Runtime 1.x was to embed a certain sequence of bytes, and you see that in the model, you attribute the cause to that exploit). This is a niche scenario, but advanced IR teams should be ready to delve into model internals at the opcode level if needed, essentially performing a reverse-engineering of the model’s “program.”

Attribution via Code and Infrastructure Analysis

After or during timeline reconstruction, IR should analyze the malicious payload itself for attribution clues. This means studying the code that was embedded in the model (if obtainable via disassembly or memory) and any IoCs. If the payload was a known malware family (e.g., Cobalt Strike beacon, Meterpreter, etc.), threat intel can attribute it to a threat group or at least classify the intent (espionage vs crimeware). In HiddenLayer’s example, the beacon had a watermark linking it to certain APT groups​. If the malicious model opened a reverse shell to 123.45.67.89, check whose infrastructure that IP is (maybe it’s a cloud VM tied to a miner gang, or an IP previously reported in an FBI alert). Even simple clues like language in the code comments or variable naming style might hint at an origin (for instance, if the code has Russian variable names or a specific hacker handle). Attribution might also involve connecting the dots between the model and the uploader – was the model uploaded by an account name that resembles known actors? Sometimes threat actors reuse handles or certificates. If law enforcement gets involved, chain-of-custody and attribution details collected by IR will be crucial for tracing the perpetrator outside the organization’s network.

Timeline of Defense Evasion

Another aspect to document is how the attack evaded defenses, as this can inform attribution and future deterrence. For example, note if the model’s payload was obfuscated or if it exploited a vulnerability in the scanner. ReversingLabs noted how using 7z compression fooled the Hugging Face scanner​, and Trail of Bits showed how obfuscating the payload as bytecode defeated naive detection​. By adding these to the timeline (e.g., “Attacker prepared model on Jan 1 with obfuscation X to bypass PickleScan​”), you create a more complete incident picture. This can attribute a level of sophistication – script kiddie vs advanced actor – based on the evasion used. If multiple stages are present (initial model compromise, then follow-on actions), attribute each stage if possible (maybe one actor provided the poisoned model and another took advantage of the backdoor – unlikely but possible in supply chain multi-stage attacks).

Preserving Evidence and Lessons

Throughout timeline and attribution work, an IR firm must preserve evidence carefully (model file, logs, memory dumps) with cryptographic hashes and documentation, as model-based attacks might lead to novel legal cases or require involvement of platform vendors (e.g., informing Hugging Face of a malicious user upload). Part of the timeline may involve engaging with those third parties (time stamped: “Notified Hugging Face of malicious model X; they removed it; learned it had 100 downloads” – which might in turn help identify other victims). Sharing sanitized attribution findings with industry groups or an ISAC can also help others (for instance, letting others know, “Be on the lookout for models containing this code snippet or contacting this IP”).

The ultimate goal is that, by the end of the investigation, the IR team can tell a coherent story of the breach from the model’s perspective: how it got in, what it did, when and how it was detected, and who was likely behind it. Building such a timeline ensures nothing is missed and is invaluable for post-incident reporting and improving defenses.

Differentiating with ML-Native IR Capabilities

As AI threats rise, organizations must rethink their digital forensics and incident response (DFIR) strategies. Incident response capabilities that incorporate native awareness of machine learning (ML) artifacts are becoming essential.

Expertise & Training in AI Security

Organizations should ensure that their incident responders are trained in ML-specific attack methods. This includes understanding serialization risks in PyTorch, ONNX, TensorFlow, and how these frameworks may be abused to execute malicious payloads. Creating a dedicated AI security function within the IR team that tracks threats and maintains internal ML security playbooks can dramatically improve detection and remediation speed.

Building Toolsets for Model Analysis

Security teams should invest in or build internal tools for scanning and analyzing ML models. Lightweight model disassemblers and opcode extractors can reveal embedded threats in .pt, .pkl, .onnx, and other formats. Organizations can also consider open-sourcing safe versions of such tools to benefit the broader community. A centralized repository of known-bad model patterns or IoCs (an "ML Threat Atlas") would also support faster triage.

Collaborating with AI Platforms

Where possible, defenders should engage with cloud-based ML service providers (e.g., Hugging Face, Azure ML, Sagemaker) to ensure visibility into model ingestion pipelines and scanning tools. Industry collaboration can help shape better default defenses and incident response hooks — such as alerting when a model triggers unexpected behaviors at runtime.

Sharing Detection Content

Defenders can contribute to the community by publishing detection rules tailored to ML environments. Sigma rules for ML-related process behaviors, YARA rules for serialized model structures, and Splunk queries for anomaly detection are all valuable. Sharing real-world detection techniques helps elevate the entire security ecosystem.

Sharing Case Studies and Knowledge

Publishing sanitized incident reports involving ML threats contributes to industry-wide readiness. Case studies detailing how poisoned models were discovered, investigated, and remediated help others learn and adapt. Conferences, blogs, and training workshops are powerful avenues for this type of knowledge transfer.

Integrating ML Scenarios into IR Drills

Tabletop exercises and simulations should include AI threat scenarios. For example, inject a poisoned model into the environment and evaluate how quickly the IR team detects and attributes the source. Such drills reveal procedural gaps and improve coordination between security teams and data science functions.

Advocating for Safer AI Practices

Organizations can champion the adoption of safer model formats (like safetensors), model signing, and secure loading practices. Just as document formats and scripting runtimes evolved for safety, so too must the tools and pipelines that power machine learning.

"In essence, ML-native IR capabilities mean having the right knowledge, tools, and mindset to tackle incidents involving AI systems. We believe in defining what incident response in the age of AI looks like, and ensuring that malicious ML models become a manageable part of the threat landscape rather than an insidious unknown. It will be impossible to take the journey alone. It is an inevitable all-industry call-to-action." added Ido Naor, CEO & Founder of Security Joes.