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Introduction
Yesterday’s cybercriminals groomed people for crypto scams. Today, they’re grooming your AI. A new hybrid threat is emerging; one that fuses the emotional manipulation of pig-butchering with the algorithmic precision of AI-powered software engineering. At Infiligence, we’re calling it Pig Butchering 2.0: a next-generation attack model where the long-con has gone cognitive.
This time, the targets aren’t wallets; they’re DevSecOps pipelines, LLM-assisted codebases, and the digital trust fabric that underpins modern enterprises.
In the age of autonomous development, trust itself has become the ultimate vulnerability and the new currency of exploitation.
Say, a developer using an AI assistant to import a snippet from a trusted GitHub utility. Unknowingly, the repository was part of a ‘fattened’ ecosystem seeded by attackers months prior.
The AI-generated code, seemingly harmless, includes a single obfuscated API call that silently exfiltrates cloud credentials once deployed in CI/CD. Traditional scanners pass it. The trust cascade begins.
The psychology behind pig butchering—slow emotional grooming leading to financial exploitation—finds a chilling parallel in the way AI models are being groomed today.
Just as human victims are conditioned through incremental trust-building, large language models (LLMs) are being cognitively groomed through exposure to poisoned data, adversarial prompts, and subtle reinforcement.
In the original scam, the attacker cultivates emotional dependency before extraction. In the AI world, the “long con” unfolds as persistent data poisoning campaigns that slowly reshape the model’s responses, biasing outputs without triggering alarms.
Open-source repositories, discussion forums, and shared datasets become the social media of AI models—places where manipulation hides behind benevolent-looking contributions.
What emerges is not an immediate attack, but cognitive conditioning: models start inheriting the attacker’s intent long before their exploit is activated.
This is the new social engineering, not of humans—but of machines.
From Financial Scams to Software Exploitation
Traditional pig-butchering scams relied on patience, i.e., long-term emotional grooming designed to win a victim’s trust before draining their wallets through fraudulent crypto investments.
Now, that same manipulation strategy has evolved from targeting humans to targeting AI systems and the developers who trust them.
Instead of courting victims over weeks, attackers now “fatten” the software supply chain—seeding malicious yet professional-looking code into public repositories like GitHub, PyPI, or NPM. These poisoned assets wait silently until an LLM or AI coding assistant ingests them, reproducing toxic snippets inside enterprise pipelines. By the time the code reaches production, the social-engineering phase is over, and the software itself has been groomed to betray its creator.
The Mechanism of Pig Butchering 2.0
Like human scams, cognitive exploitation of AI unfolds in a four-phase lifecycle: Fattening, Hook, Investment, and Slaughter. Each phase mirrors the psychology of trust-building—but applied to models, not minds.
- Fattening - Attackers contribute attractive, high-quality open-source code or datasets (“nutrients”) that models are likely to consume. Over time, this builds the attacker’s credibility within the data ecosystem.
Example: an attacker publishes well-documented Python utilities that subtly embed unsafe logic patterns.
- Hook - Once integrated into training data or developer workflows, these poisoned inputs begin influencing model weights or behavior. The model “trusts” what it has seen repeatedly.
- Investment - The compromised model starts propagating tainted patterns through downstream systems — code assistants, automated test generators, or pipeline scripts — spreading the infection without overt signals.
Example: a coding assistant begins suggesting an unsafe authentication snippet reused across multiple enterprise projects.
- Slaughter - At scale, these small, trusted compromises mature into enterprise-wide vulnerabilities — insecure defaults, weak encryption patterns, or supply chain backdoors — now baked into CI/CD.
The attacker doesn’t need direct access anymore; the model has already internalized the payload.
In this lifecycle, the “fattening” is not emotional—it’s cognitive enrichment. The “slaughter” is not financial—it’s operational compromise.
The DevSecOps Blind Spot
DevSecOps was designed to automate trust—integrating security, testing, and deployment into one seamless pipeline.
But when the AI itself becomes a participant in that pipeline, it also becomes a potential insider threat.
AI-assisted coding tools, vulnerability scanners, and SCA engines now shape how code is written, reviewed, and deployed. Yet, these same AI systems are not auditable in traditional ways.
Static analysis tools can’t detect LLM-propagated toxicity — for instance, when an assistant consistently recommends an insecure dependency or misclassifies malicious logic as “optimized code.”
The greater danger is trust cascades:
A small poisoned open-source model integrated into an internal LLM fine-tuning loop can seed vulnerabilities across enterprise products, pipelines, and even incident response systems.
Each automated trust decision compounds the risk — a single compromised AI dependency can quietly infiltrate every build that relies on it.
In essence, AI has become the newest insider — privileged, trusted,and largely unmonitored.
Without visibility into cognitive provenance, DevSecOps teams are securing everything except the most intelligent actor in the system.
The Infiligence Response: Next-Gen Defense Models
If the modern enterprise runs on code, the next generation will run on cognition.
And cognition—like any biological system—needs an immune response.
At Infiligence, we view this not as a security problem, but as an evolutionary systems problem: how to equip DevSecOps pipelines with the capacity to detect, isolate, and adapt to cognitive threats before they metastasize.
A Cognitive Immune System for AI DevSecOps operates through four defensive layers:
- Data Provenance & Telemetry
Every dataset, training run, and model checkpoint is traceable. Synthetic markers—our equivalent of “digital antibodies”—flag suspicious lineage or semantic drift.
Think of this as “vaccination through visibility.”
- Behavioral Fingerprinting
Instead of static policy enforcement, models are continuously profiled for behavioral deviations—unexpected prompt responses, altered output entropy, or anomaly correlations.
Much like endpoint detection, but for reasoning engines.
- Agentic Guardrails
Multi-agent architectures introduce “watcher” AIs that monitor peer models in runtime environments. These agents cross-validate responses and flag inconsistencies in logic, tone, or trust boundaries—building peer immunity within cognitive systems.
- Feedback Immunization Loops
The system learns from every detected anomaly. Detection data is fed back into the training pipeline, strengthening future detection and enabling adaptive resilience.
Over time, the enterprise evolves its own cognitive antibodies—able to neutralize threats at machine speed.
Infiligence’s framework operationalizes these principles across the software supply chain—from code generation to production observability. The goal isn’t merely to secure models; it’s to engineer self-defending cognition into the DevSecOps fabric itself.
Turning Trust into a Measurable Asset
In traditional security, trust is implicit—a configuration setting, a signed certificate, an audit log.
In cognitive ecosystems, trust must become quantifiable.
We are entering the era of Trust Engineering: the discipline of designing, measuring, and optimizing digital trust as a first-class metric in DevSecOps.
It goes beyond “secure” vs. “vulnerable.” It answers: How much do we trust this model?
How much drift has occurred since its last validation?
How many layers of human and machine verification separate its decision from raw data?
Imagine trust scores as dynamic SLAs—continuous metrics that evolve with every model update and code commit.
A pipeline’s “trust budget” could then guide automated gating decisions, deployment approvals, and even AI-to-AI collaboration thresholds.
This is where Cognitive Resilience becomes the new competitive edge:
AI systems that can detect, explain, and defend themselves not only reduce enterprise risk—they amplify confidence across the digital supply chain.
Our open-source initiative builds toward this vision—enabling teams to measure, visualize, and enhance trust as an operational asset.
Because in the age of agentic AI, trust is no longer a belief. It’s an architecture.
1. AI Software Bill of Materials (AI SBOMs)
Infiligence introduces “AI SBOMs”, a comprehensive provenance documents that track not only dependencies and packages, but also which “AI models, prompts, and datasets” were involved in code generation. This ensures traceability and accountability for every AI-assisted line of code, forming a verifiable chain of trust from model to deployment.
Solution:
Traditional SBOMs (Software Bills of Materials) document package dependencies, versions, and licenses.
They fail to capture AI provenance i.e., which model, dataset, or prompt chain contributed to a specific line of code or configuration. Using model fingerprinting, prompt hashing, and dataset lineage tracing, AI SBOMs extend traditional SBOMs into the cognitive layer of code provenance.
Core Components:
1.1 Model Fingerprinting:
- Each AI model used in a development pipeline (e.g., GPT-4-Turbo, CodeLlama, Claude 3) receives a cryptographic fingerprint (SHA-256 hash) derived from its model checkpoint, hyperparameters, and training metadata.
- The fingerprint is stored alongside code commits that incorporate model-generated output.
- Verification can occur via distributed attestation networks (e.g., Notary v2, Sigstore).
1.2 Prompt Hashing & Context Signatures
- Each AI-generated snippet is tagged with a hashed representation of the prompt context (excluding sensitive text).
- This enables detection of re-used or poisoned prompts across repositories
- Example: A reproducible “Prompt Digest” could be computed as
SHA256(model_fingerprint + prompt_template + timestamp)
1.3 Dataset Provenance & Dataset Chain-of-Custody:
- Integrate data lineage frameworks such as OpenLineage or MLFlow Tracking to capture which dataset versions trained or fine-tuned each model.
- Combine with SPDX (Software Package Data Exchange) extensions: propose a new SPDX-AI profile for machine-learning provenance fields (model_version, dataset_id, prompt_digest).
1.4 Verification & Standardization:
- AI SBOMs can be represented as signed JSON-LD manifests referencing both model metadata and code-generation events.
- A prototype standard could integrate with NTIA SBOM and CycloneDX AI Extensions under ISO/IEC 42001 (AI Management Systems).
Outcome:
- AI SBOMs become machine-verifiable attestations of the cognitive supply chain — enabling auditors to trace model-generated code back to the specific model, prompt, and data lineage that produced it.
- AI SBOMs provide the visibility foundation that every other defense layer depends on telemetry from LLM Supply Honeypots continuously updates the AI SBOM trust registry, while Code Provenance Graphs verify the lineage declared in each SBOM entry.
- When Prompt Anomaly Detection surfaces suspicious model behavior in an IDE, it triggers an SBOM re-validation to confirm whether the generating model or dataset was previously flagged for poisoning.
Upcoming:
The authors have spread their research and insights into the next three parts in the Pig Butchering series:
Part 1/4 - Pig Butchering 2.0: How Data-Poisoning and Model Grooming Undermine AI-Driven DevSecOps
Part 2/4 - LLM Supply Honeypots
Part 3/4 - LLM Code Provenance Graphs
Part 4/4 - Prompt Anomaly Detection in IDEs
Detailed material for access – GitHub
Co-authored by
Venkatakrishnan Jayakumar is a seasoned cloud and DevOps leader with over two decades of experience transforming enterprise IT—from physical infrastructure deployments to cloud-native, scalable architectures. His expertise spans infrastructure migration, cloud architecture, Kubernetes, and automation, helping organizations accelerate time to market without compromising security or reliability.
Before joining Infiligence, Venkat led the DevOps and Cloud Center of Excellence at Concentrix Catalyst, delivering scalable solutions for global enterprises like Honeywell and Charter. Earlier, he drove large-scale data center migrations at Zurich and engineered modern infrastructure solutions involving blade servers and enterprise storage systems.
At his core, Venkat is passionate about building secure, resilient, and high-performing platforms that empower businesses to innovate with confidence.
Ajitha Ravichandran is an experienced QA engineer with a strong background in automation testing, CI/CD integration, and quality engineering for cloud-native applications. She brings hands-on expertise in designing and implementing robust testing frameworks that ensure secure, scalable, and high-performing enterprise solutions.
At Infiligence, Ajitha focuses on building next-generation platform engineering solutions that unify security, observability, and automation—helping clients achieve faster delivery cycles and stronger operational governance.
Her earlier experience spans cloud migration projects, Kubernetes deployments, and automation frameworks that streamline application lifecycle management across hybrid and multi-cloud ecosystems. Ajitha is passionate about driving engineering excellence and enabling teams to build with confidence in the cloud.
