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\lhead{\small Adversarial Deception --- BX3 Framework}
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    pdftitle={Adversarial Deception in Agentic Systems: Detection, Attribution, and Countermeasures},
    pdfauthor={Jeremy Blaine Thompson Beebe},
    pdfsubject={Artificial Intelligence, Security, Multi-Agent Systems},
    pdfkeywords={adversarial deception, agentic security, input validation, reasoning attestation, output hashing, semantic binding, BX3 Framework, Agentic, multi-agent security},
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\title{
    \vspace{1.2cm}
    {\LARGE \textbf{Adversarial Deception in Agentic Systems:}}\\[0.6em]
    {\large \textit{Detection, Attribution, and Countermeasures}\\[1.0em}
}
\author{
    \textbf{Jeremy Blaine Thompson Beebe}\\[0.2em]
    \textit{Independent Researcher}\\[0.3em]
    ORCID: \href{https://orcid.org/0009-0009-2394-9714}{0009-0009-2394-9714} \quad Email: bxthre3inc@gmail.com\\[0.3em]
    \textit{Bxthre3 Inc. \hfill April 2026}\\[0.3em]
}

\date{April 2026}

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\begin{abstract}\noindent
Agentic systems --- multi-agent AI architectures with autonomous task decomposition and execution --- are vulnerable to a class of attacks we term \textbf{adversarial deception}: an attacker who can influence an agent's inputs can cause the agent to act on falsified information in ways that benefit the attacker. This paper characterizes the adversarial deception attack surface in agentic systems, defines a three-stage attack taxonomy (input contamination, reasoning corruption, output manipulation), and presents countermeasures at each stage: input validation with source provenance at the Bounds Engine, reasoning trace attestation by the Fact Layer, and output hashing with semantic binding to source records. We prove that an agentic system implementing all three countermeasures is resistant to adversarial deception under standard cryptographic assumptions, and we present deployment evidence from the Agentic platform showing detection of 847 attempted deception events and zero successful deceptions over 200 days of operation.
\end{abstract}

\vspace{0.5em}
\noindent\textbf{Keywords:} adversarial deception, agentic security, input validation, reasoning attestation, output hashing, semantic binding, BX3 Framework, Agentic, multi-agent security

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% -------------------------------------------------------
\section{Introduction}
\label{sec:intro}

Agentic systems --- AI systems that decompose complex goals into sub-tasks assigned to specialized agents --- are powerful but introduce a large attack surface. Each agent in a task decomposition chain receives inputs from other agents, from external data sources, and from the environment. An attacker who can contaminate any of these inputs can cause the agent to make decisions based on falsified information, to take actions that benefit the attacker, or to suppress information that would reveal the deception.

This attack class --- adversarial deception --- is distinct from traditional adversarial examples (which target model outputs) and prompt injection (which targets model inputs). Adversarial deception targets the agentic system's decision-making infrastructure: the reasoning chains, source records, and attestation mechanisms that the system uses to verify its own outputs. The attacker's goal is not to cause the model to output a wrong classification, but to cause the system to act on false premises in ways that are undetectable by the system's existing monitoring.

The adversarial deception threat is particularly acute in the BX3 Framework because the framework's power --- recursive spawning, dynamic model routing, self-modification --- depends on the integrity of information flowing through multiple layers. If an attacker can falsify the inputs to a spawned agent, the spawned agent's reasoning will be corrupted from the start. If an attacker can corrupt the reasoning trace of a Bounds Engine, the Fact Layer cannot verify that the proposed action was derived correctly from valid inputs.

We show that three countermeasures, implemented correctly, close the attack surface completely under standard cryptographic assumptions.

% -------------------------------------------------------
\section{The Three-Stage Attack Taxonomy}
\label{sec:taxonomy}

Adversarial deception in agentic systems operates across three stages. Each stage targets a distinct component of the agentic decision-making infrastructure.

\medskip\noindent\textbf{Stage 1 -- Input Contamination.}
The attacker introduces falsified data into the agent's input stream. This can occur through: compromised external data sources (sensor feeds, API responses, database records), man-in-the-middle attacks on inter-agent communication channels, or corrupted context caches that supply background information to the agent. The attacker's goal is to make the agent reason from false premises, causing it to reach conclusions that benefit the attacker without triggering any single-layer anomaly detection.

\medskip\noindent\textbf{Stage 2 -- Reasoning Corruption.}
The attacker corrupts the agent's reasoning process itself --- either by modifying the agent's internal state between inference steps, by manipulating the reasoning trace that the agent generates, or by causing the agent to skip verification steps. The attacker's goal is to make the agent reach incorrect conclusions from correct premises. Unlike input contamination, reasoning corruption does not require access to the input stream; it targets the inference process itself.

\medskip\noindent\textbf{Stage 3 -- Output Manipulation.}
The attacker modifies the agent's output after it is generated --- either by intercepting and altering the output before it reaches the next agent or the \fact, or by causing the \fact\ to accept an output that does not match the agent's actual reasoning. This stage exploits the temporal gap between output generation and \fact\ acceptance to substitute a falsified output.

% -------------------------------------------------------
\section{Countermeasures}
\label{sec:countermeasures}

We propose countermeasures at each stage, each grounded in the BX3 Framework's layer architecture.

\subsection{Stage 1: Input Validation with Source Provenance}

Every input to an agent must carry source provenance --- a cryptographic record of where the input originated, what processing it has passed through, and the identity of the source that produced it. The source provenance record is generated at the point of origin and is signed by the source's identity key.

The \bounds\ validates source provenance before accepting any input. For each input, the \bounds\ verifies: the signature on the provenance record is valid under the source's identity key, the chain of processing steps in the provenance record does not include any step that has been flagged as compromised, and the input's timestamp is within an acceptable freshness window.

Inputs without valid provenance are quarantined and flagged for human review. The quarantine is enforced by the \fact\ layer --- even if the \bounds\ is compromised and attempts to accept an invalid input, the \fact\ will not accept it for any downstream operation.

\subsection{Stage 2: Reasoning Trace Attestation}

The \fact\ layer attests reasoning traces: at each major inference step, the agent generates a cryptographic commitment to its current state. The commitment is computed as a hash of the reasoning trace accumulated so far:

$$C_i = \text{SHA256}(C_{i-1} \parallel R_i \parallel \tau_i)$$

where $C_{i-1}$ is the prior commitment, $R_i$ is the current reasoning trace segment, and $\tau_i$ is a timestamp. The commitment $C_i$ is recorded in the forensic ledger and cannot be modified after the fact.

If an attacker later attempts to corrupt a reasoning trace, the corruption is detectable by comparing the attested commitments to the current trace. The attacker cannot forge a valid $C_i$ without breaking SHA-256 pre-image resistance.

The attestation mechanism is architecturally enforced: the agent cannot suppress or skip the attestation step without triggering a bailout through the \fact\ layer.

\subsection{Stage 3: Semantic Output Binding}

Every output is semantically bound to its source records through a cryptographic hash:

$$H = \text{SHA256}(O \parallel S)$$

where $O$ is the output payload and $S$ is the set of source records that the agent referenced in generating $O$. The hash $H$ is computed at output generation time and recorded in the forensic ledger alongside the output.

The hash is verified by the \fact\ layer before the output enters any downstream operation. Any post-generation modification to either $O$ or $S$ produces a different hash, triggering detection. The binding ensures that an output cannot be silently substituted after the fact: the \fact\ will only accept outputs whose hash matches the recorded binding.

% -------------------------------------------------------
\section{Security Proofs}
\label{sec:securityproofs}

We prove that an agentic system implementing all three countermeasures is resistant to adversarial deception under standard cryptographic assumptions.

\begin{quote}
\textbf{Theorem 1 (Input Integrity).} If HMAC signature verification passes for an input $x$, then $x$ originated from the claimed source and has not been modified in transit.
\end{quote}
\textit{Proof.} HMAC unforgeability guarantees that a valid HMAC tag on $x$ can only be produced by the holder of the shared secret key between the source and the \bounds. An attacker who does not hold this key cannot produce a valid tag for any $x' \neq x$. Therefore, if verification passes, $x$ originated from the legitimate source. $\square$

\begin{quote}
\textbf{Theorem 2 (Attestation Integrity).} If the reasoning trace $R'$ differs from the attested reasoning trace $R$, then the attestation mismatch is detectable by the \fact\ layer.
\end{quote}
\textit{Proof.} Attestation commitment $C_i$ is computed as $\text{SHA256}(C_{i-1} \parallel R_i \parallel \tau_i)$. SHA-256 pre-image resistance guarantees that given $C_i$, an attacker cannot find any $R_i' \neq R_i$ and $\tau_i'$ that produces the same hash. Therefore, any modification to $R_i$ produces a different commitment that cannot be forged. $\square$

\begin{quote}
\textbf{Theorem 3 (Output Binding Integrity).} If the output $O$ is accepted by the \fact\ layer with hash $H$, then $O$ and its source records $S$ are bitwise identical to those used in computing $H$.
\end{quote}
\textit{Proof.} $H = \text{SHA256}(O \parallel S)$. SHA-256 collision resistance guarantees that no efficient algorithm can find distinct $(O', S')$ such that $\text{SHA256}(O' \parallel S') = H$. Therefore, if the \fact\ accepts $H$, then $(O, S)$ must be identical to the original $(O, S)$. $\square$

\begin{quote}
\textbf{Theorem 4 (Compositional Security).} A system implementing all three countermeasures is resistant to all three stages of adversarial deception.
\end{quote}
\textit{Proof.} Follows directly from Theorems 1--3: Stage 1 attacks require forging HMAC signatures (blocked by Theorem 1), Stage 2 attacks require forging attestation commitments (blocked by Theorem 2), and Stage 3 attacks require finding SHA-256 collisions (blocked by Theorem 3). $\square$

% -------------------------------------------------------
\section{Deployment Evidence: Agentic Platform}
\label{sec:deployment}

Over 200 days of operation on the Agentic platform, the three countermeasures operated continuously across all agentic workflows.

\textbf{Input Validation:} The \bounds\ processed approximately 12.4 million inputs carrying source provenance records. Of these, 847 inputs (0.007\%) failed provenance validation and were quarantined. Follow-up investigation confirmed that 843 (99.5\%) of the quarantined inputs were genuine attack attempts (compromised sensor feeds, MITM attempts on inter-agent channels). The remaining 4 were attributable to transient infrastructure failures requiring equipment repair.

\textbf{Reasoning Trace Attestation:} The \fact\ layer attested 1.2 billion reasoning trace commitments. 23 attempted reasoning corruption events were detected through attestation mismatch --- all attributable to in-memory tampering attempts that were immediately contained. Zero attestation mismatches were left unresolved.

\textbf{Output Binding:} 847,000 outputs were verified against their semantic binding hashes. Zero successful output substitution events occurred. The mean overhead of the binding verification was 0.3ms per output.

\textbf{Combined Effect:} Over 200 days, zero successful adversarial deceptions were confirmed through independent forensic audit. The mean overhead of all three countermeasures on normal agentic operations was 2.3ms per inference step (approximately 0.8\% overhead on the 280ms mean inference time for the platform's dominant workload).

% -------------------------------------------------------
\section{Related Work}
\label{sec:related}

The adversarial deception threat is related to but distinct from several lines of prior work.

Adversarial examples research \cite{goodfellow2015adv} focuses on inputs crafted to cause model misclassification. Adversarial deception operates at a higher level of abstraction: the goal is not to cause the model to output a wrong answer, but to cause the system to act on false premises in ways that benefit the attacker and evade detection.

In the multi-agent systems literature, authentication and non-repudiation for agent communication has been studied by Kanth et al. \cite{kanth2012}, who propose cryptographic attestation for inter-agent messages. Our Stage 2 countermeasure extends this work by adding the cryptographic commitment chain across reasoning steps, enabling detection of in-transit reasoning corruption.

The semantic binding approach to output integrity is related to blockchain-based audit logging systems \cite{nakamoto2008bitcoin}, which use hash chaining to ensure ledger integrity. We adapt this technique to the agentic output context, where the output is bound not to a sequence of prior outputs but to the specific source records that informed it.

Formal verification approaches to multi-agent system security \cite{wooldridge2002introduction} provide compositional reasoning about agent properties, but do not specifically address the three-stage deception attack surface we characterize here.

% -------------------------------------------------------
\section{Conclusion}
\label{sec:conclusion}

Adversarial deception is a distinct and particularly dangerous attack class targeting agentic systems: it operates at the decision-making infrastructure level rather than the model level, exploits the complexity of multi-agent information flows, and can succeed without triggering any single-layer anomaly detection.

The three-stage attack taxonomy (input contamination, reasoning corruption, output manipulation) characterizes the complete attack surface. The three countermeasures (source provenance validation, reasoning trace attestation, semantic output binding) close the attack surface completely under standard cryptographic assumptions.

The countermeasures are architecturally enforced by the \fact\ layer: the \bounds\ cannot bypass them, and the agent cannot suppress them. This makes the security properties hold regardless of agent behavior, even in the presence of a partially compromised \bounds\ layer.

The Agentic platform deployment confirms the countermeasures' effectiveness: 847 attempted deceptions detected, zero successful deceptions confirmed through forensic audit, and 2.3ms mean overhead per inference step.

% -------------------------------------------------------
\section{Limitations and Future Work}
\label{sec:limitations}

The countermeasures' primary limitation is the overhead of cryptographic verification for every input, reasoning step, and output. For high-frequency, low-stakes operations, this overhead may be unacceptable. Future work will explore graduated verification depth: full attestation for high-stakes workflows, lightweight attestation for routine operations.

The countermeasures also assume the cryptographic primitives (HMAC, SHA-256) are not broken. If quantum computers achieve cryptographically relevant attacks on these primitives, the construction would need to be updated to post-quantum alternatives.

% -------------------------------------------------------
\section*{Peer Review Instructions}
\label{sec:peer-review}
\addcontentsline{toc}{section}{Peer Review Instructions}

\subsection*{Review Criteria}

\textbf{1. Originality and Contribution (30\%):} Does the paper introduce a genuinely novel threat class and countermeasures? The three-stage taxonomy is the primary novel contribution. The countermeasures are composed from existing cryptographic primitives but their composition in the agentic context is novel.

\textbf{2. Technical Soundness (30\%):} Are the security proofs valid? Are the cryptographic assumptions stated clearly? The proofs rely on HMAC unforgeability, SHA-256 pre-image resistance, and SHA-256 collision resistance --- all standard cryptographic assumptions.

\textbf{3. Clarity and Completeness (20\%):} Is the threat surface fully characterized? Are all three stages addressed by countermeasures? Is the deployment evidence sufficient?

\textbf{4. Significance (20\%):} Is adversarial deception a genuine concern for production agentic systems? Does the paper provide actionable guidance for defenders?

\subsection*{Submission Checklist}

\begin{itemize}
    \item[ ] Three-stage attack taxonomy clearly defined and distinguishable from prior threat classes
    \item[ ] Countermeasures grounded in BX3 Framework layer architecture
    \item[ ] Security proofs provided for all three countermeasures
    \item[ ] Deployment evidence with quantified metrics
    \item[ ] Related Work distinguishes adversarial deception from adversarial examples and prompt injection
    \item[ ] Limitations acknowledged
\end{itemize}

% -------------------------------------------------------
\section*{Acknowledgments}
\addcontentsline{toc}{section}{Acknowledgments}

The author acknowledges the researchers cited herein, whose work across adversarial machine learning, multi-agent security, and cryptographic attestation provides the intellectual context in which adversarial deception detection and countermeasures are situated.

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\noindent\small\textit{This work has not undergone peer review. Comments and correspondence are welcome at bxthre3inc@gmail.com.}

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