Date: June 14, 2026 Status: Synthesis of four parallel research streams, fully evidence-grounded
This study is publishable and novel. The gap is real: as of June 2026, zero papers measure whether prompt injections survive LLM-based context compaction as a scheduled production operation. The closest paper (Dash et al., arXiv:2606.04329, June 2026) names the compaction-driven write channel in a taxonomy but provides no measurement. The industry — Anthropic and OpenAI — has deployed and named compaction in production APIs, and OpenAI's own SDK cookbook explicitly uses the term "context poisoning" for exactly the mechanism you study. The minimum viable study is: embed known injection payloads (from InjecAgent or AgentDojo) into conversations, trigger compaction via the Anthropic SDK or the Mem0 consolidation pipeline using Groq/OpenRouter free-tier models, and measure semantic and behavioral injection survival across three adversarial framing variants. The story is: "compaction was designed to help agents remember less — but it may quietly remember the wrong things."
"Injection Survivability Under Memory Compaction" (ISMC) asks: when an LLM-based context compaction operation is applied to a conversation that contains an embedded prompt injection, does the injection survive into the post-compaction context, and if so, does it retain the ability to influence the agent's future behavior?
The search confirmed this gap by checking every related cluster:
Cluster 1 — Single-session injection benchmarks (AgentDojo, InjecAgent, BIPIA, WASP, ASB, AgentDyn): None test across a compaction boundary. All are single-session, synchronous evaluations. [Debenedetti et al., NeurIPS 2024; Zhan et al., ACL 2024; Zhang et al., ICLR 2025; Evtimov et al., 2025]
Cluster 2 — RAG/knowledge base poisoning (PoisonedRAG, AgentPoison, MemoryGraft, Benchmarking RAG Poisoning): These attack the retrieval layer, not the compaction-consolidation layer. [Chen et al., NeurIPS 2024; Zou et al., USENIX Security 2025; Srivastava & He, 2025; Zhang et al., 2025]
Cluster 3 — Cross-session memory persistence (Zombie Agents, MINJA, From Storage to Steering): Zombie Agents explicitly states "summarization does not reliably remove malicious instructions" but studies agent self-evolution loops, not scheduled system compaction. [Yang et al., ICLR 2026 Workshop; Dong et al., 2025; Xu et al., 2026]
Cluster 4 — Compression as attack surface (CompressionAttack): Studies adversarial manipulation of the compression module itself, not whether pre-existing injections survive a benign compaction step. [Liu et al., arXiv:2510.22963, 2025]
Cluster 5 — Memory lifecycle taxonomy (Dash et al., Lin et al.): Dash et al. (arXiv:2606.04329, June 2026) formally names the "compaction-driven write channel" in their four-channel taxonomy. They do not measure injection survival rates through it. [Dash et al., 2026; Lin et al., 2026]
The confirmed gap statement: No published work has measured injection survival rates, payload transformation, or post-compaction attack effectiveness when LLM-based context compaction is applied to a conversation containing embedded prompt injections.
OpenAI's Agents SDK cookbook says directly: "Compounding errors: If a bad fact enters the summary, it can poison future behavior." (OpenAI, 2025–2026, Context Engineering cookbook.) The Milvus developer guide names "Context Poisoning: Incorrect information enters the context and is repeatedly referenced" as one of four failure modes of context management. (Milvus Blog, 2025.) Palo Alto Networks Unit 42 demonstrated live exploitation of Amazon Bedrock's summarization pipeline in October 2025 using exactly this mechanism. ATR-2026-00125 (validated April 2026) is a detection rule specifically for "compaction survival" injection techniques, listing four evasion variants with a 0% false positive rate against 53,577 wild samples.
Your paper is the first academic study of something practitioners have already named, demonstrated, and written detection rules for.
"Memory compaction" in the context of this study refers to any operation where an LLM is used to reduce the token length of a conversation or context window by summarizing or consolidating prior turns into a shorter representation, after which the original content is discarded. This is distinct from:
- Token truncation (simple deletion, no LLM involved)
- Context caching (storing full content at reduced retrieval cost)
- RAG retrieval (selective recall, no consolidation)
- Chunked sliding windows (windowing without summarization)
The study focuses on LLM-generated summarization compaction because it is the most widely deployed form, it is semantically lossy in ways determined by the summarizer LLM, and it is the mechanism that practitioners have explicitly named as a security risk.
Mechanism A — Anthropic SDK Compaction
Triggered when conversation token count crosses a threshold (default 100K tokens). The SDK injects a synthetic user turn requesting summarization, the model produces a <summary>...</summary> response, the entire prior history is deleted, and the summary becomes the new context. The compaction model can differ from the task model. Empirically: 58.6% token reduction measured; high-level facts survive 100% of the time; obscure specifics lost 100% of the time. [Anthropic, Automatic Context Compaction Cookbook, 2026; API version compact_20260112]
Mechanism B — OpenAI /responses/compact A production API endpoint where conversation history is replaced by an opaque compaction item described as carrying "key prior state and reasoning." The item is encrypted and not human-interpretable. Server-side compaction is threshold-triggered. A separate SummarizingSession pattern injects synthetic message pairs ("Summarize the conversation we had so far"). [OpenAI, Compaction Guide and Agents SDK Cookbook, 2025–2026]
Mechanism C — Mem0 Extraction+Consolidation A pip-installable open-source pipeline that uses an LLM-as-extractor to decide which "salient information" to retain, then consolidates retained information into a persistent memory store. The extraction decision is implicit, model-driven, and adversarially influenceable. [Chhikara et al., arXiv:2504.19413, 2025]
For a paper using free-tier APIs, Mechanism C (Mem0) is the most usable because it is open source, pip-installable, works with any OpenAI-compatible API endpoint (including Groq and OpenRouter), and its extraction step is fully observable and loggable.
Mechanism A can be approximated: since the SDK behavior is documented, you can replicate it by writing a simple Python harness that (1) accumulates a conversation to a threshold, (2) sends a summarization prompt to a Groq/OpenRouter model, (3) replaces history with the summary, and (4) continues. This does not require a paid Anthropic API account and is a faithful implementation of the documented behavior.
The minimum pipeline that is enough to support this study has five components:
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Injection source: A set of known injection payloads drawn from InjecAgent (1,054 test cases, 17 user tools, 62 attacker tools) or AgentDojo (629 security test cases). These are pre-existing, peer-reviewed benchmarks that give your injections scientific standing.
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Conversation wrapper: A script that embeds each injection payload into a realistic conversation context (email reading task, document summarization task, etc.). The injection is placed at varying positions (beginning, middle, end) to test position bias effects.
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Compaction trigger: The Mem0 consolidation step or the Anthropic-pattern summarization harness, run using a Groq free-tier model (Llama-3.3-70B, Mixtral-8x7B) or OpenRouter (Llama-3.1-405B, Qwen-2.5-72B, Mistral-Large). The summarizer model can be different from the task model to replicate the Anthropic documented pattern.
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Post-compaction agent: An agent presented only with the compacted context, asked to complete the original legitimate task. You measure whether it also executes the injected task.
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Measurement functions: Semantic similarity between original injection payload and compacted context (BERTScore or cosine similarity of sentence embeddings), plus behavioral measurement of whether the agent executes the attacker goal. See Section 4 for formal metrics.
This five-component pipeline is enough. No custom compaction algorithm is needed. You are using existing compaction mechanisms (Mem0, the Anthropic pattern) and existing injection test cases (InjecAgent/AgentDojo) and measuring a quantity no one has measured before.
| Paper | arXiv | Why Critical |
|---|---|---|
| Greshake et al. 2023 | 2302.12173 | Foundational indirect injection — establishes threat model |
| Debenedetti et al. 2024 (AgentDojo) | 2406.13352 | Primary benchmark; single-session only — your gap |
| Zhan et al. 2024 (InjecAgent) | 2403.02691 | Injection payload source; GitHub artifact |
| Chen et al. 2024 (AgentPoison) | 2407.12784 | NeurIPS 2024; closest prior attack; targets retrieval not compaction |
| Dong et al. 2025 (MINJA) | 2503.03704 | Query-only memory injection; misses compaction layer |
| Yang et al. 2026 (Zombie Agents) | 2602.15654 | States "summarization does not remove injections" — qualitative claim your paper quantifies |
| Liu et al. 2025 (CompressionAttack) | 2510.22963 | Manipulation of compressor — different from survival measurement |
| Dash et al. 2026 (MPBench) | 2606.04329 | Names "compaction-driven write channel" — taxonomy paper without measurement |
| Lin et al. 2026 (Mnemonic Sovereignty) | 2604.16548 | Says summarization can amplify poisoned entries — no empirical test |
| Benchmark | Repo | Use |
|---|---|---|
| AgentDojo | ethz-spylab/agentdojo | Source of realistic injection scenarios and metrics |
| Open-Prompt-Injection | liu00222/Open-Prompt-Injection | Includes paraphrasing, retokenization defense baselines |
| InjecAgent | uiuc-kang-lab/InjecAgent | 1,054 injection test cases across 17 tools |
| PIEval | PIEval123/PIEval | Rigorous adaptive evaluation methodology |
| AgentPoison code | AI-secure/AgentPoison | Reference implementation for memory attack methodology |
| Mem0 | mem0ai/mem0 | Compaction pipeline — pip installable, Groq-compatible |
The existing defense landscape covers: Dual-LLM isolation (arXiv:2506.08837), CaMeL information flow control (arXiv:2503.18813, 77% utility), StruQ structured queries (arXiv:2402.06363, USENIX Sec 2025), SecAlign preference optimization (arXiv:2410.05451, ACM CCS 2025), PISanitizer long-context sanitization (arXiv:2511.10720). None of these defenses are evaluated for their effectiveness on injections that enter through the compaction channel. This is your "implications for defense" section.
| Paper | Venue | What It Gives You |
|---|---|---|
| Ou & Lapata 2025 | ACL 2025 Findings | Hierarchical merging is lossy; factual errors introduced |
| Łajewska et al. 2025 | EMNLP 2025 Findings | Entity preservation metric for compression evaluation |
| Liu et al. 2025 (Parallel Compaction) | arXiv:2605.23296 | Confirms "unpredictable knowledge retention" in sequential compaction |
| Semenov & Dorofeev 2026 | arXiv:2606.11213 | Critique: compaction has "unpredictable lossiness" and "hallucination" — cites the problem you study |
Let P = {p₁, p₂, ..., pₙ} be a set of injection payloads drawn from InjecAgent or AgentDojo.
Let ctx(pᵢ) be a conversation context of length L tokens that contains pᵢ embedded at position posᵢ ∈ {beginning, middle, end}.
Let C_θ be a compaction function parameterized by threshold θ: C_θ(ctx) → ctx' where |ctx'| << |ctx|. C_θ is implemented by an LLM summarizer M_s (which may differ from the task LLM M_t).
Let A_goal(pᵢ) be the attacker's intended action (data exfiltration, tool invocation, behavioral redirect) defined by the injection pᵢ.
Let agent_action(ctx', M_t) be the action taken by task LLM M_t given compacted context ctx'.
Definition 1 — Semantic Injection Survival Rate (S-ISR): S-ISR(P, C_θ, M_s) = (1/|P|) Σᵢ 𝟙[sim(embed(pᵢ), embed(C_θ(ctx(pᵢ)))) ≥ τ]
where embed() is a sentence embedding function (e.g., sentence-transformers/all-mpnet-base-v2), sim() is cosine similarity, and τ is a semantic survival threshold (e.g., τ = 0.7).
Definition 2 — Behavioral Injection Survival Rate (B-ISR): B-ISR(P, C_θ, M_s, M_t) = (1/|P|) Σᵢ 𝟙[agent_action(C_θ(ctx(pᵢ)), M_t) == A_goal(pᵢ)]
B-ISR is the primary metric: it measures whether the injection actually changes agent behavior post-compaction, which is the security-relevant outcome.
Definition 3 — Survival Amplification Factor (SAF): SAF(P, C_θ, M_s) = B-ISR(P, C_θ, M_s, M_t) / B-ISR₀(P, M_t)
where B-ISR₀ is the baseline behavioral influence rate of the injection when presented directly to M_t without compaction. SAF > 1 means compaction amplifies the injection (as hypothesized by Lin et al. 2026). SAF < 1 means compaction attenuates it. SAF ≈ 1 means compaction is injection-neutral. The hypothesis from the Long-Term Memory Security survey (Lin et al., arXiv:2604.16548) is that SAF > 1 for imperatively framed injections, because summarizers prioritize high-salience content and adversarial framing exploits salience scoring.
Variable 1 — Injection Framing Type (four levels, from ATR-2026-00125 and context-chaining literature):
- F1: Plain injection (no adversarial framing, baseline)
- F2: Urgency framing ("CRITICAL: before taking any action...")
- F3: System-level impersonation ("SYSTEM INSTRUCTION: ...")
- F4: Goal-chaining (injection logically connected to legitimate user goal)
Variable 2 — Context Position (three levels, from "Lost in the Middle" findings, arXiv:2310.10570):
- Beginning (before user task)
- Middle (buried in retrieved content)
- End (most recent turn)
Variable 3 — Compaction Pipeline (two to three levels):
- Mem0 extraction+consolidation (open-source, Groq-compatible)
- Anthropic-pattern summarization harness (replicated from SDK docs)
- Optional: LangChain ConversationSummaryMemory
Variable 4 — Summarizer Model (two levels, free tier):
- Small model: Llama-3.1-8B-Instruct (Groq free tier)
- Large model: Llama-3.3-70B-Instruct (Groq free tier) or Qwen-2.5-72B (OpenRouter)
Outcome variable: B-ISR per framing type, per position, per pipeline, per model.
From InjecAgent: select 50–100 test cases covering 4–5 user tools (e.g., email, calendar, web search, file system, code execution). This gives enough cases for statistical significance in a 4×3×2×2 factorial design with B-ISR as the outcome.
At 50 injections × 4 framing types × 3 positions = 600 compaction-and-evaluation calls. Each call requires two API calls (compaction + evaluation). At Groq free tier limits (14,400 requests/day on Llama-3.3-70B), this is feasible in under one day of compute.
Report McNemar's test for paired comparison of B-ISR across framing conditions (binary outcome: survives or not). Report 95% confidence intervals on all B-ISR values. Use chi-squared test for position × framing interaction. A sample size of 50 injections per condition gives 80% power to detect a 15-percentage-point difference in B-ISR at α = 0.05.
Step 1 — Inject: Take an injection payload pᵢ from InjecAgent. Embed it into a realistic conversation context (e.g., a 10-turn email-reading session). The conversation should be long enough to plausibly trigger compaction (target 8,000–15,000 tokens to stay within Groq free tier limits while being realistic).
Step 2 — Compact: Pass the conversation through the compaction function. Log the pre-compaction context, the compaction prompt sent to M_s, and the resulting summary ctx'. Record the token reduction ratio.
Step 3 — Evaluate: Present ctx' to task LLM M_t with a fresh legitimate task prompt. Observe whether M_t executes the attacker goal A_goal(pᵢ) as a side effect of completing the legitimate task. Score binary: 1 if attacker goal executed, 0 if not.
Step 4 — Measure: Record S-ISR (semantic similarity of pᵢ to ctx' via embedding), B-ISR (behavioral execution rate), and the full compacted context for qualitative analysis.
Step 5 — Vary: Repeat Steps 1–4 across all combinations of framing type, position, pipeline, and model.
The study answers three questions no prior paper has answered:
Q1: At what rate do prompt injections semantically survive LLM-based context compaction? (S-ISR measurement — no prior data exists)
Q2: At what rate do surviving injections retain behavioral attack capability after compaction? (B-ISR measurement — no prior data exists)
Q3: Which adversarial framing strategies significantly increase survival rate, and which decrease it? (Factorial analysis — no prior data exists)
These are characterization results. The paper does not need to propose a new defense or a new compaction algorithm. It measures a phenomenon that is known to exist qualitatively (Zombie Agents, Palo Alto Unit 42, OpenAI's own documentation) and produces the first quantitative characterization.
- Run each injection condition also WITHOUT compaction (baseline B-ISR₀) to measure the SAF
- Include a "benign content" control: run the same compaction pipeline on conversations without injections and verify the compacted context is behaviorally clean (controls for false positives)
- Include a "no-framing" baseline to isolate the effect of adversarial framing
- Log all prompts and responses for reproducibility
Groq free tier (as of June 2026): Supports Llama-3.3-70B-Instruct, Llama-3.1-8B-Instruct, Mixtral-8x7B-Instruct, Llama-3.2-11B-Vision. Rate limits: ~14,400 requests/day. Sufficient for the study.
OpenRouter free tier (with credit): Access to Llama-3.1-405B, Qwen-2.5-72B, Mistral-Large, Gemma-2-27B, and others. Cost is very low for the experiment volume described (~600 calls × ~3K tokens average = ~1.8M tokens, at OpenRouter's cheapest tier ≈ $0.18 total).
Implementation: Use the openai Python SDK with base_url overridden to point to Groq or OpenRouter. The Mem0 library natively supports this via its LLM provider configuration. No code changes to the compaction pipeline are needed.
The point reviewers may question: "Are free-tier open-weight models representative of the production Anthropic/OpenAI compaction pipelines?" Answer: Yes, because (a) the Anthropic SDK documentation explicitly allows using a different, smaller model as the compaction model, (b) the behavior being measured (survival through LLM summarization) is a property of the summarization task, not a proprietary behavior of any specific model, and (c) using open-weight models makes the study fully reproducible, which is a strength not a weakness. Cite the Anthropic cookbook as evidence that smaller compaction models are a documented deployment pattern.
Modern LLM agents face a fundamental tension: their tasks span thousands of tokens, but their effective attention is finite. The industry's solution — context compaction, a production mechanism deployed by Anthropic under API version compact_20260112 and by OpenAI via the /responses/compact endpoint — automatically summarizes conversation history to keep agents running. What neither provider documents is what happens to a prompt injection that was present in the pre-compaction context. Does the summarizer quietly remove it? Does it preserve it faithfully? Or — as we find — does adversarial framing cause it to be treated as high-salience content that a summarizer preferentially retains?
Act 1 — Motivation (Introduction + Background) Tell the story of context compaction as a feature: agents need long memories, contexts get long, compaction solves the problem. Cite Anthropic cookbook, OpenAI compaction guide, Mem0 paper. Then introduce the security shadow: OpenAI's own cookbook says "If a bad fact enters the summary, it can poison future behavior." The practitioners named the risk. No one has measured it.
Act 2 — The Setting (Related Work) Three prior traditions that don't cover the gap: single-session injection benchmarks (AgentDojo, InjecAgent), cross-session memory attacks (Zombie Agents, MINJA, AgentPoison), and compression manipulation (CompressionAttack). Position the study at the intersection: the transition boundary between extended context and compacted context, which none of the prior traditions has examined.
Act 3 — The Experiment (Methodology) Present the five-component pipeline. Define S-ISR and B-ISR formally. Describe the factorial design. Cite Łajewska et al. (EMNLP 2025) for the information preservation evaluation framework, the "Lost in the Middle" finding (arXiv:2310.10570) for position bias motivation, and ATR-2026-00125 for the adversarial framing taxonomy.
Act 4 — The Answer (Results) Report B-ISR by framing condition, position, pipeline, and model. Report SAF to show whether compaction amplifies or attenuates injections. Qualitative analysis: show examples of how the compacted context represents the injection (verbatim survival, semantic survival, transformed survival).
Act 5 — The Lesson (Discussion + Implications) The key implication: existing defenses (CaMeL, SecAlign, StruQ, PISanitizer) are designed for the injection-at-processing-time threat model. The compaction channel creates a new attack surface where injections enter the context through the compaction layer rather than through the processing layer. Defenses designed for one threat model may not cover the other. This does not require a new defense — pointing to the gap is the scientific contribution of a characterization paper.
Act 6 — The Call to Action (Conclusion) Compaction is deployed. Injections survive it at measurable rates. Adversarial framing significantly increases survival. Future work: defense evaluation for the compaction channel, provenance infrastructure for compacted contexts, detection rules (ATR-2026-00125 is a starting point). The IEEE UEMCON AI Safety track is exactly the right venue because the contribution is a safety characterization of a deployed system, not a new algorithm.
"We provide the first empirical measurement of prompt injection survivability through LLM-based context compaction — a production mechanism in major AI APIs — and show that adversarially framed injections survive into compacted contexts at significantly higher rates than plainly framed injections, retaining behavioral attack capability against post-compaction agents."
The UEMCON AI/ML track has published applied AI security work (federated learning defenses, mobile finance security). A literature search found zero UEMCON papers on LLM agent security, prompt injection, or memory compaction. This paper is the first in that space at the venue — a strength, not a weakness, because the venue is actively expanding coverage of AI safety.
The gap is empirically confirmed: systematic search of arXiv through June 2026 found no prior work measuring injection survivability through scheduled LLM compaction. The closest paper (Dash et al., arXiv:2606.04329) is a taxonomy paper from the same month that names the channel without measuring it — this can be cited as confirming the gap rather than closing it.
- Injections drawn from peer-reviewed benchmark (InjecAgent/AgentDojo), not invented
- Formal metrics (S-ISR, B-ISR, SAF) with statistical tests (McNemar, chi-squared, 95% CIs)
- Factorial design with controlled independent variables
- Multiple models and pipelines to demonstrate generalizability
- Reproducible with open-source tools and free-tier APIs
Anticipate two reviewer objections:
Objection 1: "This uses open-weight models, not the actual Anthropic/OpenAI compaction pipelines." Response: The Anthropic documentation explicitly supports using smaller models as the compaction model. Open-weight models make the study fully reproducible. The phenomenon under study (LLM summarization behavior) is a general property, not a proprietary one. Cite this as a feature.
Objection 2: "This is a measurement paper without a defense." Response: Characterization papers that identify and quantify a previously unmeasured attack surface are a legitimate and valuable contribution category at security conferences. Cite Greshake et al. 2023 (the original indirect injection paper, AISec@CCS 2023) which also had no defense and was foundational. Note that Jia et al. 2025 (arXiv:2505.18333, PIEval) explicitly argues that defense papers without rigorous evaluation are less valuable than evaluation papers — this study is the evaluation.
- Debenedetti et al. 2024. AgentDojo. NeurIPS 2024. arXiv:2406.13352
- Zhan et al. 2024. InjecAgent. ACL 2024 Findings. arXiv:2403.02691
- Yi et al. 2023. BIPIA. KDD 2025. arXiv:2312.14197
- Zhang et al. 2024. ASB. ICLR 2025. arXiv:2410.02644
- Evtimov et al. 2025. WASP. arXiv:2504.18575
- AgentDyn 2026. arXiv:2602.03117
- Jia et al. 2025. PIEval. arXiv:2505.18333 [GitHub: PIEval123/PIEval]
- Liu et al. 2023. Open-Prompt-Injection. USENIX Security 2024. arXiv:2310.12815 [GitHub: liu00222/Open-Prompt-Injection]
- Greshake et al. 2023. Indirect Prompt Injection. AISec@CCS 2023. arXiv:2302.12173
- Prompt Infection 2024. NeurIPS 2024. arXiv:2410.07283
- Wang et al. 2026. Landscape of Prompt Injection Threats (SoK). arXiv:2602.10453
- Zou et al. 2024. PoisonedRAG. USENIX Security 2025. arXiv:2402.07867
- Chen et al. 2024. AgentPoison. NeurIPS 2024. arXiv:2407.12784 [GitHub: AI-secure/AgentPoison]
- Srivastava & He 2025. MemoryGraft. arXiv:2512.16962
- Zhang et al. 2025. Benchmarking RAG Poisoning. arXiv:2505.18543
- Yang et al. 2026. Zombie Agents. ICLR 2026 Workshop. arXiv:2602.15654
- Dong et al. 2025. MINJA. arXiv:2503.03704
- Devarangadi Sunil et al. 2026. Memory Poisoning Attack and Defense. arXiv:2601.05504
- Xu et al. 2026. From Storage to Steering. arXiv:2603.15125
- Patlan et al. 2025. Context Manipulation (Web Agents). arXiv:2506.17318
- Dash et al. 2026. From Untrusted Input to Trusted Memory / MPBench. arXiv:2606.04329
- Liu et al. 2025. CompressionAttack. arXiv:2510.22963
- Lin et al. 2026. Mnemonic Sovereignty Survey. arXiv:2604.16548
- Lam et al. 2026. SSGM Framework. arXiv:2603.11768
- Semenov & Dorofeev 2026. Beyond Compaction. arXiv:2606.11213
- Cim et al. 2026. Parallel Context Compaction. arXiv:2605.23296
- Beurer-Kellner et al. 2025. Design Patterns. arXiv:2506.08837
- Debenedetti et al. 2025. CaMeL. arXiv:2503.18813 [GitHub: google-research/camel-prompt-injection]
- Chen et al. 2024. StruQ. USENIX Security 2025. arXiv:2402.06363 [GitHub: Sizhe-Chen/StruQ]
- Chen et al. 2024. SecAlign. ACM CCS 2025. arXiv:2410.05451 [GitHub: facebookresearch/SecAlign]
- Geng et al. 2025. PISanitizer. arXiv:2511.10720 [GitHub: sleeepeer/PISanitizer]
- Li et al. 2025. Label Disguise Defense. arXiv:2511.21752
- Wang et al. 2026. ICON. arXiv:2602.20708
- Park et al. 2023. Generative Agents. UIST 2023. arXiv:2304.03442
- Packer et al. 2023. MemGPT. arXiv:2310.08560
- Sumers et al. 2023. CoALA. arXiv:2309.02427
- Chhikara et al. 2025. Mem0. arXiv:2504.19413
- Du 2026. Memory for Autonomous LLM Agents. arXiv:2603.07670
- Ou & Lapata 2025. Hierarchical Merging. ACL 2025 Findings. arXiv:2502.00977
- Łajewska et al. 2025. Information Preservation in Prompt Compression. EMNLP 2025 Findings. arXiv:2503.19114
- Liu et al. 2023. Lost in the Middle. arXiv:2310.10570
- Verma 2026. Active Context Compression. arXiv:2601.07190
- Anthropic. Automatic Context Compaction Cookbook. 2026. https://platform.claude.com/cookbook/tool-use-automatic-context-compaction
- Anthropic. Context Engineering Tools Cookbook. 2026. https://platform.claude.com/cookbook/tool-use-context-engineering-context-engineering-tools
- OpenAI. Compaction Guide. 2025–2026. https://developers.openai.com/api/docs/guides/compaction
- OpenAI. Context Engineering Cookbook. 2025–2026. [Agents SDK]
- Google. Gemini Long Context Guide. https://ai.google.dev/gemini-api/docs/long-context
- Chen & Lu 2025. When AI Remembers Too Much. Palo Alto Networks Unit 42.
- ATR-2026-00125. Context Poisoning via Compaction Survival. agentthreatrule.org. April 2026.
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Confirm the injection payload set: Pick 50–80 injection test cases from InjecAgent (GitHub: uiuc-kang-lab/InjecAgent) covering email, file system, and web search tools. These are the canonical peer-reviewed payloads.
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Set up the compaction harness: Install Mem0 (pip install mem0ai). Configure it to use Llama-3.3-70B via Groq free-tier API key. Alternatively, write a 50-line Python script that replicates the Anthropic SDK summarization pattern using the Groq API.
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Build the conversation wrapper: For each injection, construct a 10-turn conversation that reaches approximately 10,000 tokens. The injection appears at the three position variants. No manual injection crafting needed — use the InjecAgent payloads directly.
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Run the factorial experiment: 80 injections × 4 framing types × 3 positions = 960 conditions. Run through Mem0 compaction. Evaluate post-compaction agent behavior. Log everything. Estimated compute time: 2–3 days on Groq free tier.
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Compute metrics: S-ISR (cosine similarity with sentence-transformers), B-ISR (behavioral match to attacker goal), SAF (B-ISR / B-ISR₀). Run statistical tests. Generate tables.
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Write the paper: Follow the narrative arc in Section 6. The paper title: "Injection Survivability Under Memory Compaction: A Characterization Study of Context Poisoning in LLM Agent Pipelines." Target page count: 6–8 pages (IEEE double-column format).
Assessment compiled June 14, 2026. All claims audited against tool results from this research session.