Adversarial Forensics: When the Attacker Builds the Evidence

Contributed by Kris Carlson, COO, Former ICAC Commander, Digital Forensics Investigator, and Testifying Expert

Series context. This is Part 1 of the Forensics and Futures 2026 series. It introduces a shift that investigators can no longer treat as an edge case: adversaries are not just hiding evidence, they are constructing it, poisoning it, and steering investigators toward a false narrative. [1]

The Rise of Adversarial Forensics

Digital forensics has historically relied on a simple premise: systems contain artifacts that reflect what actually happened. That premise is now under active pressure from adversarial behavior and AI-enabled manipulation.

Two forces are converging:

• Adversarial manipulation of analytic systems (including ML-based detection and triage) that can be exploited through evasion, poisoning, and other adversarial techniques. [2]
• Operational realities that reduce visibility and increase ambiguity, including cloud-first architectures, large-scale incident volumes, and fast-moving threat ecosystems that reward speed over certainty. [3]

The result is not merely “less evidence.” The greater concern is false confidence: accepting manipulated logs, screenshots, or synthetic media as authentic because they “look right” and pass automated or initial checks.

LCG perspective. Historically, one of the most significant risks to digital investigators was missing evidence or improper collection, which led to challenges; however, this has changed.  Now, a primary threat and a new reality is accepting falsified evidence as genuine because it survives automated verification and appears plausible.

The New Adversary Playbook

Attackers have shifted from concealing their actions to actively building an alternate reality inside the systems we investigate. Three developments dominate modern forensic risk.

1. AI-Assisted Log Manipulation

Threat actors can modify or insert log entries, alter timestamps, fabricate authentication events, and rewrite metadata to mimic normal behavior. At a minimum, this is “classic” anti-forensics. Increasingly, it is also model-aware manipulation: attackers shape artifacts to evade detection systems and mislead triage.

NIST’s adversarial machine learning taxonomy is sound here because it provides a shared vocabulary for what practitioners are seeing in the field, including evasion techniques and data poisoning patterns that undermine analytic reliability. [2]

When logs are plausibly altered, authentication becomes a courtroom issue, not just a technical issue. In U.S. proceedings, authentication requirements under FRE 901 and reliability challenges under FRE 702 become immediate pressure points. [4][5] Daubert reliability principles often surface when an expert must explain why a method is trustworthy despite potential manipulation. [6]

2. Synthetic Media and Deepfake Evidence

Synthetic media is no longer limited to high-profile politics or celebrity hoaxes. Investigators now encounter fabricated or altered items, including:

• screenshots and “conversation” exports
• audio clips and voice notes
• video segments, screen recordings, and surveillance derivatives
• “proof” packaged for HR, compliance, fraud, and criminal complaints

NIST has directly evaluated analytic systems against AI-generated deepfakes and documented challenges like robustness, laundering/post-processing, and generalization across evolving generative methods. [7] This matters because many organizations still treat images, recordings, and message exports as self-authenticating “common sense” evidence. They are not.

3. Automated Anti-Forensic Techniques

Anti-forensics is a mature discipline that includes the destruction, concealment, and counterfeiting of evidence. [8] For some time now, the forensics community has recognized that attackers can exploit assumptions in forensic tools and create deliberate ambiguity that slows or misdirects investigators through anti-forensic data manipulation. [9]

What changes in 2026 are scale and accessibility. Automation and AI assistance make these techniques cheaper, faster, and more repeatable. The realistic assumption for incident response and investigations is now: the environment may be adversarially manipulated before you ever image a disk or pull a log.

Evidentiary Implications: Integrity at Risk

The standards and legal expectations have not changed. The environment has.

• ISO/IEC 27037 sets expectations for the identification, collection, acquisition, and preservation of digital evidence. [10]
• NIST provides practical guidance on integrating forensic techniques into incident response to enable organizations to collect and preserve evidence without compromising operational control. [11]
• U.S. legal admissibility hinges on authentication and reliability, commonly grounded in FRE 901, FRE 702, and Daubert considerations. [4][5][6]

Adversarial manipulation targets each pillar:

• Integrity becomes uncertain when artifacts may be synthetically generated or altered.
• Authenticity becomes ambiguous when timestamps, log sequences, or media can be convincingly forged.
• Reliability becomes contestable when tool output is treated as proof instead of as an intermediate analytic step.

This is why documentation and defensibility matter as much as extraction. If you cannot explain how you validated integrity, your opponent can often reframe your “evidence” as a hypothesis.

The Human Examiner as the Assurance Layer

Automation helps with speed. It does not solve adversarial authenticity. Three examiner-centered practices should be treated as a baseline.

1. Manual Integrity Validation

At a minimum, examiners should deliberately test whether the story told by artifacts is consistent across independent sources. That may include:

• correlating logs across multiple systems (endpoint, identity, network, cloud control plane)
• validating timestamps against external references and known data
• checking for gaps, discontinuities, and sequences that are statistically “too neat.”
• evaluating potential clock manipulation and timeline artifacts

Peer-reviewed work on timestamp and timeline forgery demonstrates why this is necessary: timeline manipulation is present, tools exist, and forensic interpretation can be misled without careful validation. [12]

2. Provenance-Based Analysis

Modern investigations must explicitly address provenance:

• What system generated the artifact?
• What level of access would be required to alter it?
• Is there corroboration from an independent source?
• Are you relying on a single artifact class (e.g., screenshots) without support?

NIST’s scientific foundation review of digital investigation techniques underscores the importance of grounding methods and conclusions in reliable principles and documented practice. [13]

3. Examiner Testimony and Defensibility

When adversarial manipulation is plausible, defensibility requires the examiner to articulate:

• What manipulation risks were considered
• What validation tests were performed
• What corroboration exists across sources
• How the chain of custody and preservation were maintained
• Why were alternative explanations ruled out

Taken together, these elements establish a transparent and defensible basis for examiner testimony by demonstrating that potential manipulation was considered, tested, corroborated, and reasonably excluded through documented forensic practice.

Frameworks, Pitfalls, and Mitigation Strategies

Relevant frameworks

• ISO/IEC 27037 digital evidence handling guidance [10]
• NIST SP 800-86 incident response and forensics integration [11]
• NIST digital evidence preservation considerations [15]
• FRE 901 and FRE 702 [4][5]
• Daubert reliability principles [6]
• EU Artificial Intelligence Act (for governance expectations around high-impact AI systems and accountability) [16]
• DOJ electronic evidence manual [14]

Common pitfalls

• Over-reliance on automated triage as “truth.”
• Accepting screenshots, exports, or single-system logs without authentication
• Weak preservation documentation for cloud and remote artifacts
• Failure to document integrity checks and corroboration steps
• Treating synthetic media as a “special case” instead of a normal risk condition

Mitigation strategies

• Build adversarial integrity checks into the standard workflow, not as an exception path.
• Require examiner oversight of AI-generated or AI-analyzed outputs
• Preserve cloud evidence with cryptographic verification and repeatable export procedures where possible [15]
• Train investigators, HR, and counsel on synthetic media realities and authentication requirements [7]
• Operationalize cross-functional collaboration between forensics, cybersecurity, and legal early in the process [11][14]

Quick Checklist

1. Treat all digital evidence as potentially manipulated until validated.
2. Rely on provenance and corroboration, not single artifacts.
3. Document integrity testing as deliberately as extraction.

Final Thought

Attackers have learned that evidence is not only something to erase. It is something to shape. In 2026, forensic readiness means assuming manipulation is plausible and proving authenticity through disciplined, defensible methodology.

References (endnotes)

[1] Forensics and Futures 2026 Series Outline. LCG Discovery internal planning document.

[2] National Institute of Standards and Technology (NIST). Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025). (PDF)
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2025.pdf (NIST Publications)

[3] European Union Agency for Cybersecurity (ENISA). ENISA Threat Landscape 2024. (PDF)
https://www.enisa.europa.eu/sites/default/files/2024-11/ENISA%20Threat%20Landscape%202024_0.pdf (ENISA)

[4] Legal Information Institute (Cornell Law School). Federal Rules of Evidence, Rule 901: Authenticating or Identifying Evidence.
https://www.law.cornell.edu/rules/fre/rule_901 (Legal Information Institute)

[5] Legal Information Institute (Cornell Law School). Federal Rules of Evidence, Rule 702: Testimony by Expert Witnesses.
https://www.law.cornell.edu/rules/fre/rule_702 (Legal Information Institute)

[6] Legal Information Institute (Cornell Law School). Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993).
https://www.law.cornell.edu/supct/html/92-102.ZS.html (Legal Information Institute)

[7] Guan, H., Horan, J., & Zhang, A. (NIST). Guardians of Forensic Evidence: Evaluating Analytic Systems Against AI-Generated Deepfakes. Forensics@NIST 2024. (PDF)
https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959128

[8] Rogers, M. K., et al. Arriving at an Anti-forensics Consensus: Examining How to Define and Control the Anti-forensics Problem. DFRWS USA 2006. (PDF)
https://dfrws.org/sites/default/files/session-files/2006_USA_paper-arriving_at_an_anti-forensics_consensus_-_examining_how_to_define_and_control_the_anti-forensics_problem.pdf (DFRWS)

[9] Garfinkel, S. Anti-Forensics: Techniques, Detection and Countermeasures. ICIW 2007. (PDF)
https://calhoun.nps.edu/bitstream/handle/10945/44248/Garfinkel_Anti-Forensics_2007.ICIW.AntiForensics.pdf?sequence=1 (calhoun.nps.edu)

[10] ISO. ISO/IEC 27037:2012, Guidelines for identification, collection, acquisition and preservation of digital evidence.
https://www.iso.org/standard/44381.html (ISO)

[11] NIST. SP 800-86: Guide to Integrating Forensic Techniques into Incident Response.
https://csrc.nist.gov/pubs/sp/800/86/final (NIST Computer Security Resource Center)

[12] Prokos, A., et al. Time for Truth: Forensic Analysis of NTFS Timestamps. (PDF)
https://eprints.cs.univie.ac.at/7091/1/3465481.3470016.pdf (eprints.cs.univie.ac.at)

[13] NIST. Digital Investigation Techniques: A NIST Scientific Foundation Review (NIST IR 8354). (PDF)
https://nvlpubs.nist.gov/nistpubs/ir/2022/NIST.IR.8354.pdf (NIST Publications)

[14] U.S. Department of Justice (DOJ), CCIPS. Searching and Seizing Computers and Obtaining Electronic Evidence in Criminal Investigations (2009). (PDF)
https://www.justice.gov/criminal/cybercrime/docs/ssmanual2009.pdf (Department of Justice)

[15] NIST. Digital Evidence Preservation: Considerations for Evidence Handlers (NIST IR 8387). (PDF)
https://nvlpubs.nist.gov/nistpubs/ir/2022/NIST.IR.8387.pdf (NIST Publications)

[16] European Union. Regulation (EU) 2024/1689 (Artificial Intelligence Act), Official Journal (PDF).
https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ%3AL_202401689 (EUR-Lex)